Practice2-1_DataAnalysis_Preprocessing_TimeSeries_KK.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **알고리즘의 데이터 학습을 위한 전처리**\n",
    "\n",
    "[![Open in Colab](http://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/thekimk/All-About-Time-Series-Analysis/blob/main/Practice2-1_DataAnalysis_Preprocessing_TimeSeries_KK.ipynb)\n",
    "\n",
    "<center><img src='Image/Advanced/Bigdata_Algorithm.png' width='600'></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 시계열 처리(Time Series Processing)\n",
    "\n",
    "**1) Time Series Analysis:** `미래를 예측하려는 인간의 의지`로 출발하였으며, 우리의 삶 모든 곳에서 `시간 흐름에서 벗어날 수 없고` 이를 이해하고 해석하여 `의미있는 패턴`을 찾아 미래를 예측하는 기술\n",
    "\n",
    "- **시계열 데이터(Time Series Data):** `일정한 시간 간격`에 따라 `순차적(Sequentially)으로 기록`된 `확률적 프로세스` 또는 `시간변화 데이터`\n",
    "\n",
    "> - 시간의 흐름에 따라 `불규칙적 변동`을 분석하기 위해 `필수적 데이터`\n",
    "> - `과거`가 `미래`에 어떤 영향을 주는지 `분석을 통해 예측` 가능\n",
    "> - 최근 `기계학습과 딥러닝`을 사용하여 `복잡한 데이터 예측`\n",
    "> - 시계열예측과 기계학습/딥러닝 간 `전처리와 알고리즘 방식 차이` 때문에, 별도로 `(1) 시계열 데이터분석 단계 이해, (2) 시계열 데이터의 전처리, (3) 시계열 알고리즘 이해` 필수!\n",
    "\n",
    "<center><img src='Image/Advanced/TS_Example1.png' width='900'></center>\n",
    "<center><img src='Image/Advanced/TS_Example2.png' width='900'>(https://www.ecb.europa.eu/pub/financial-stability/fsr/html/ecb.fsr202205~f207f46ea0.en.html#toc6)</center>\n",
    "\n",
    "---\n",
    "\n",
    "**2) 시계열 전처리(Time Series Preprocessing):**\n",
    "\n",
    "> **\"`시간패턴` 이해와 적용을 위해 `시간특징을 추출하고 반영`하여 `문제의 용도와 알고리즘에 맞게` 사전에  처리\"**\n",
    ">\n",
    "> - 최근 `기계학습과 딥러닝`을 사용하여 `복잡한 데이터 예측`\n",
    "> - 기계학습/딥러닝과 시계열예측 간 `전처리와 알고리즘 반영 차이` 때문에, `시계열 변수생성 또는 알고리즘은 별도의 관점 이해` 필수!\n",
    "\n",
    "<center><img src='Image/Advanced/TS_MS_Comparison.png' width='900'></center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **데이터 불러오기(Data Loading)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:06.897969Z",
     "start_time": "2022-08-07T14:23:06.885972Z"
    }
   },
   "outputs": [],
   "source": [
    "# # Colab에서 실행\n",
    "# # 아래 코드 실행 후 파일 선택을 눌러서 \n",
    "# # 'Food_Agriculture_Organization_UN_Full.csv' 파일을 선택해주면 로딩 완료\n",
    "# from google.colab import files\n",
    "# files.upload()\n",
    "\n",
    "# # 아래 코드 실행 시 'Food_Agriculture_Organization_UN_Full.csv' 데이터 출력 가능\n",
    "# import pandas as pd\n",
    "# df = pd.read_csv('Food_Agriculture_Organization_UN_Full.csv')\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:08.103973Z",
     "start_time": "2022-08-07T14:23:06.899974Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area Abbreviation</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Item Code</th>\n",
       "      <th>Item</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Y2004</th>\n",
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       "      <th>Y2006</th>\n",
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       "      <th>Y2008</th>\n",
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       "      <th>0</th>\n",
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       "      <td>Wheat and products</td>\n",
       "      <td>5142</td>\n",
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       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>3249.0</td>\n",
       "      <td>3486.0</td>\n",
       "      <td>3704.0</td>\n",
       "      <td>4164.0</td>\n",
       "      <td>4252.0</td>\n",
       "      <td>4538.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>4711.0</td>\n",
       "      <td>4810</td>\n",
       "      <td>4895</td>\n",
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       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>419.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>425</td>\n",
       "      <td>422</td>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>2513</td>\n",
       "      <td>Barley and products</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>367</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2513</td>\n",
       "      <td>Barley and products</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>185.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2514</td>\n",
       "      <td>Maize and products</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>120.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\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",
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       "      <th>21472</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2948</td>\n",
       "      <td>Milk - Excluding Butter</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>373.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>426</td>\n",
       "      <td>451</td>\n",
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       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
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       "      <td>Fish, Seafood</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
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       "      <td>15</td>\n",
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       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Fish, Seafood</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>21475</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Aquatic Products, Other</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>21476</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2928</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "<p>21477 rows × 24 columns</p>\n",
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      ],
      "text/plain": [
       "      Area Abbreviation  Area Code         Area  Item Code  \\\n",
       "0                    AF          2  Afghanistan       2511   \n",
       "1                    AF          2  Afghanistan       2805   \n",
       "2                    AF          2  Afghanistan       2513   \n",
       "3                    AF          2  Afghanistan       2513   \n",
       "4                    AF          2  Afghanistan       2514   \n",
       "...                 ...        ...          ...        ...   \n",
       "21472                ZW        181     Zimbabwe       2948   \n",
       "21473                ZW        181     Zimbabwe       2960   \n",
       "21474                ZW        181     Zimbabwe       2960   \n",
       "21475                ZW        181     Zimbabwe       2961   \n",
       "21476                ZW        181     Zimbabwe       2928   \n",
       "\n",
       "                           Item  Element Code Element         Unit  latitude  \\\n",
       "0            Wheat and products          5142    Food  1000 tonnes     33.94   \n",
       "1      Rice (Milled Equivalent)          5142    Food  1000 tonnes     33.94   \n",
       "2           Barley and products          5521    Feed  1000 tonnes     33.94   \n",
       "3           Barley and products          5142    Food  1000 tonnes     33.94   \n",
       "4            Maize and products          5521    Feed  1000 tonnes     33.94   \n",
       "...                         ...           ...     ...          ...       ...   \n",
       "21472   Milk - Excluding Butter          5142    Food  1000 tonnes    -19.02   \n",
       "21473             Fish, Seafood          5521    Feed  1000 tonnes    -19.02   \n",
       "21474             Fish, Seafood          5142    Food  1000 tonnes    -19.02   \n",
       "21475   Aquatic Products, Other          5142    Food  1000 tonnes    -19.02   \n",
       "21476             Miscellaneous          5142    Food  1000 tonnes    -19.02   \n",
       "\n",
       "       longitude  ...   Y2004   Y2005   Y2006   Y2007   Y2008   Y2009   Y2010  \\\n",
       "0          67.71  ...  3249.0  3486.0  3704.0  4164.0  4252.0  4538.0  4605.0   \n",
       "1          67.71  ...   419.0   445.0   546.0   455.0   490.0   415.0   442.0   \n",
       "2          67.71  ...    58.0   236.0   262.0   263.0   230.0   379.0   315.0   \n",
       "3          67.71  ...   185.0    43.0    44.0    48.0    62.0    55.0    60.0   \n",
       "4          67.71  ...   120.0   208.0   233.0   249.0   247.0   195.0   178.0   \n",
       "...          ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "21472      29.15  ...   373.0   357.0   359.0   356.0   341.0   385.0   418.0   \n",
       "21473      29.15  ...     5.0     4.0     9.0     6.0     9.0     5.0    15.0   \n",
       "21474      29.15  ...    18.0    14.0    17.0    14.0    15.0    18.0    29.0   \n",
       "21475      29.15  ...     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "21476      29.15  ...     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "        Y2011  Y2012  Y2013  \n",
       "0      4711.0   4810   4895  \n",
       "1       476.0    425    422  \n",
       "2       203.0    367    360  \n",
       "3        72.0     78     89  \n",
       "4       191.0    200    200  \n",
       "...       ...    ...    ...  \n",
       "21472   457.0    426    451  \n",
       "21473    15.0     15     15  \n",
       "21474    40.0     40     40  \n",
       "21475     0.0      0      0  \n",
       "21476     0.0      0      0  \n",
       "\n",
       "[21477 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Local PC에서 실행\n",
    "# 상대위치로 예제 데이터 로딩\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "location_rel = os.path.join('.', 'Data', 'FoodAgricultureOrganization', 'Food_Agriculture_Organization_UN_Full.csv')\n",
    "df = pd.read_csv(location_rel)\n",
    "df = df[[col for col in df.columns if col[:2] != 'Y1']]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| **변수명** | **의미** |\n",
    "|:---|:---|\n",
    "| Area Abbreviation | 국가명 축약어 |\n",
    "| Area Code | 국가코드 숫자 |\n",
    "| Area | 국가명 |\n",
    "| Item Code | 음식코드 |\n",
    "| Item | 음식 |\n",
    "| Element Code | 음식 또는 사료 코드 |\n",
    "| Element | 음식 또는 사료 |\n",
    "| Unit | 단위 |\n",
    "| Latitude | 생산위치 위도 |\n",
    "| Longitude | 생산위치 경도 |\n",
    "| Y1961 | 1961년도 생산량 |\n",
    "| Y1962 | 1962년도 생산량 |\n",
    "| $\\dots$ | $\\dots$ |\n",
    "| Y2013 | 2013년도 생산량 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **시간인덱스 처리(Time Index Preprocessing)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 시간데이터\n",
    "\n",
    "| **대분류** \t| **소분류** \t| **컴퓨터의 분류1** \t| **컴퓨터의 분류2** \t|\n",
    "|:---:\t|:---:\t|:---:\t|:---:\t|\n",
    "| **질적변수(Qualitative Variable)** \t| **-** \t| - | 범주형 |\n",
    "|  \t| **명목형 변수(Nominal Variable)** \t|  문자\t|  범주형 |\n",
    "|  \t| **순위형 변수(Ordinal Variable)** \t|  숫자\t|  범주형 |\n",
    "| **양적변수(Quantitative Variable)** \t| **-** \t| - | 연속형 |\n",
    "|  \t| **이산형 변수(Discrete Variable)** \t|  숫자\t| 연속형 |\n",
    "|  \t| **연속형 변수(Continuous Variable)** \t|  숫자\t| 연속형 |\n",
    "\n",
    "> - **문자형 vs 숫자형**: `문자`를 숫자로 바꾸어 컴퓨터가 이해가능하도록 변환\n",
    "> - **범주형 vs 연속형**: `범주형`을 알고리즘이 이해가능하도록 변환\n",
    "\n",
    "---\n",
    "\n",
    "> **\"범주형과 연속형 이외 다른 변수의 유형으로 `날짜나 시간 데이터`\"**\n",
    ">\n",
    "> **\"시간변수는 `시계열(Time Series) 또는 시퀀스(Time Sequence)`라고도 하며 데이터분석에서 중요한 역할\"**\n",
    ">\n",
    "> **\"모든 분석문제에는 `시간을 고려`할 수밖에 없지만 닥터 스트레인지가 되고 싶어하는 많은 분석가들에게 `쉽지 않은 현실`이며, 시간변수는 `시계열(Time Series) 또는 시퀀스(Time Sequence)`라고도 하며 데이터분석에서 중요한 역할\"**\n",
    ">\n",
    ">```python\n",
    "> # 시간정보 생성\n",
    "import datetime as dt\n",
    "dt.date(year, month, day)\n",
    "dt.date(year, month, day, hour, minute, second)\n",
    ">\n",
    "> import pandas as pd\n",
    "pd.Timestamp('year-month-dayThour:minute:second')\n",
    ">```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.540971Z",
     "start_time": "2022-08-07T14:23:16.527972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2108, 11, 4, 13, 30, 59)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 날짜와 시간을 다루는 datetime 모듈을 dt 라는 닉네임으로 로딩\n",
    "# 임의 시간 설정\n",
    "import datetime as dt\n",
    "\n",
    "future = dt.datetime(2108, 11, 4, 13, 30, 59)    # (year, month, day, hour, minute, second)\n",
    "future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.555971Z",
     "start_time": "2022-08-07T14:23:16.542972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2108-11-04 13:30:59'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 설정 시간을 문자로 출력\n",
    "str(future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.570971Z",
     "start_time": "2022-08-07T14:23:16.557972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2108-11-04', ' 13:30:59')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 날짜 또는 시간 추출\n",
    "str(future)[:10], str(future)[10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.585971Z",
     "start_time": "2022-08-07T14:23:16.572970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2108/11/04, 13/30/59'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 표시형식 변경하기\n",
    "future.strftime('%Y/%m/%d, %H/%M/%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.600970Z",
     "start_time": "2022-08-07T14:23:16.587970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임의 시간정보의 요일 확인\n",
    "# 일요일은 0부터\n",
    "future.strftime('%w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.615977Z",
     "start_time": "2022-08-07T14:23:16.603972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('45', '44')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임의 시간정보의 주차 확인\n",
    "# %U는 일요일이 주의 첫번째 날짜로, %W는 월요일이 주의 첫번째 날짜로\n",
    "future.strftime('%U'), future.strftime('%W')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.630971Z",
     "start_time": "2022-08-07T14:23:16.618972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2008-11-04 13:30:59')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas로 임의 시간 설정\n",
    "past = pd.Timestamp('2008-11-04T13:30:59')\n",
    "past"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.645969Z",
     "start_time": "2022-08-07T14:23:16.633973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2008-11-04 13:30:59'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 설정 시간을 문자로 출력\n",
    "str(past)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.660971Z",
     "start_time": "2022-08-07T14:23:16.649986Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2008-11-04', ' 13:30:59')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 날짜 또는 시간 추출\n",
    "str(past)[:10], str(past)[10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.675970Z",
     "start_time": "2022-08-07T14:23:16.662973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2008/11/04, 13/30/59'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 표시형식 변경하기\n",
    "past.strftime('%Y/%m/%d, %H/%M/%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.690971Z",
     "start_time": "2022-08-07T14:23:16.677971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임의 시간정보의 요일 확인\n",
    "# 일요일은 0부터\n",
    "past.strftime('%w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.705971Z",
     "start_time": "2022-08-07T14:23:16.692970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('44', '44')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임의 시간정보의 주차 확인\n",
    "past.strftime('%U'), past.strftime('%W')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.720971Z",
     "start_time": "2022-08-07T14:23:16.707971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime.datetime(2108, 11, 4, 13, 30, 59), Timestamp('2008-11-04 13:30:59'))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future, past"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.735970Z",
     "start_time": "2022-08-07T14:23:16.722973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2108-11-04 13:30:59')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Timestamp로 바꾸기\n",
    "# pd.to_datetime() 함수로 변경 가능\n",
    "pd.to_datetime(future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.750972Z",
     "start_time": "2022-08-07T14:23:16.737971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2008, 11, 4, 13, 30, 59)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datatime으로 바꾸기\n",
    "# to_pydatetime() 함수로 변경 가능\n",
    "past.to_pydatetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.765981Z",
     "start_time": "2022-08-07T14:23:16.752972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2008, 11, 4)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# date만 추출\n",
    "past.to_pydatetime().date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.781973Z",
     "start_time": "2022-08-07T14:23:16.768976Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime.date(2108, 11, 4), datetime.date(2008, 11, 4))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# date만 추출\n",
    "future.date(), past.date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.796974Z",
     "start_time": "2022-08-07T14:23:16.785985Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime.time(13, 30, 59), datetime.time(13, 30, 59))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# time만 추출\n",
    "future.time(), past.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.811970Z",
     "start_time": "2022-08-07T14:23:16.800972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime.datetime(2023, 1, 8, 0, 2, 32, 770529),\n",
       " Timestamp('2023-01-08 00:02:32.770529'))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 현재의 날짜 시간정보 추출\n",
    "dt.datetime.now(), pd.Timestamp.now()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 시간인덱스 생성\n",
    "\n",
    "- Timestamp와 datetime은 `형태나 사용방식`이 굉장히 `유사`\n",
    "- 특정 시간정보 변수를 `시계열 타입으로 변환`하거나 `Index로 사용`할때는 `DatetimeIndex` 필요\n",
    "- `DatetimeIndex`는 특정 순간에 기록된 `Timestamp` 형식의 인덱스\n",
    "> (1) `pd.date_range()`\n",
    ">\n",
    "> (2) `pd.to_datetime()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.826970Z",
     "start_time": "2022-08-07T14:23:16.814973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=21477, step=1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터의 기존 인덱스 확인\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.841971Z",
     "start_time": "2022-08-07T14:23:16.828972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-01-01', '2100-01-02', '2100-01-03', '2100-01-04',\n",
       "               '2100-01-05', '2100-01-06', '2100-01-07', '2100-01-08',\n",
       "               '2100-01-09', '2100-01-10',\n",
       "               ...\n",
       "               '2158-10-11', '2158-10-12', '2158-10-13', '2158-10-14',\n",
       "               '2158-10-15', '2158-10-16', '2158-10-17', '2158-10-18',\n",
       "               '2158-10-19', '2158-10-20'],\n",
       "              dtype='datetime64[ns]', length=21477, freq='D')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임의 시간정보 데이터 생성\n",
    "pd.date_range('2100-01-01', periods=df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.857973Z",
     "start_time": "2022-08-07T14:23:16.843972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2100-01-01 00:00:00', freq='D')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DatetimeIndex의 각 값들은 Timestamp\n",
    "pd.date_range('2100-01-01', periods=df.shape[0])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.872973Z",
     "start_time": "2022-08-07T14:23:16.860973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-01-01', '2100-01-06', '2100-01-11', '2100-01-16',\n",
       "               '2100-01-21', '2100-01-26', '2100-01-31', '2100-02-05',\n",
       "               '2100-02-10', '2100-02-15'],\n",
       "              dtype='datetime64[ns]', freq='5D')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# freq 매개변수로 원하는 빈도 설정 가능\n",
    "# 5일간격 시간인덱스 생성\n",
    "pd.date_range('2100-01-01', periods=10, freq='5d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.887969Z",
     "start_time": "2022-08-07T14:23:16.874972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-01-03', '2100-01-10', '2100-01-17', '2100-01-24',\n",
       "               '2100-01-31', '2100-02-07', '2100-02-14', '2100-02-21',\n",
       "               '2100-02-28', '2100-03-07'],\n",
       "              dtype='datetime64[ns]', freq='W-SUN')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# freq 매개변수로 원하는 빈도 설정 가능\n",
    "# 주별간격 시간인덱스 생성\n",
    "pd.date_range('2100-01-01', periods=10, freq='1w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.902971Z",
     "start_time": "2022-08-07T14:23:16.889971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-01-01', '2100-01-08', '2100-01-15', '2100-01-22',\n",
       "               '2100-01-29', '2100-02-05', '2100-02-12', '2100-02-19',\n",
       "               '2100-02-26', '2100-03-05'],\n",
       "              dtype='datetime64[ns]', freq='W-FRI')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# freq 매개변수로 원하는 빈도 설정 가능\n",
    "# 주별 특정요일간격 시간인덱스 생성\n",
    "pd.date_range('2100-01-01', periods=10, freq='W-FRI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.918974Z",
     "start_time": "2022-08-07T14:23:16.906971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-01-06', '2100-01-13', '2100-01-20', '2100-01-27',\n",
       "               '2100-02-03', '2100-02-10', '2100-02-17', '2100-02-24',\n",
       "               '2100-03-03', '2100-03-10'],\n",
       "              dtype='datetime64[ns]', freq='W-WED')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# freq 매개변수로 원하는 빈도 설정 가능\n",
    "# 주별 특정요일간격 시간인덱스 생성\n",
    "pd.date_range('2100-01-01', periods=10, freq='W-WED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.933973Z",
     "start_time": "2022-08-07T14:23:16.920973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-01-13', '2100-02-10', '2100-03-10', '2100-04-14',\n",
       "               '2100-05-12', '2100-06-09', '2100-07-14', '2100-08-11',\n",
       "               '2100-09-08', '2100-10-13'],\n",
       "              dtype='datetime64[ns]', freq='WOM-2WED')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# freq 매개변수로 원하는 빈도 설정 가능\n",
    "# 매월 2번째 특정요일간격 시간인덱스 생성\n",
    "pd.date_range('2100-01-01', periods=10, freq='WOM-2WED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.948971Z",
     "start_time": "2022-08-07T14:23:16.935972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-01-15', '2100-02-19', '2100-03-19', '2100-04-16',\n",
       "               '2100-05-21', '2100-06-18', '2100-07-16', '2100-08-20',\n",
       "               '2100-09-17', '2100-10-15'],\n",
       "              dtype='datetime64[ns]', freq='WOM-3FRI')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# freq 매개변수로 원하는 빈도 설정 가능\n",
    "# 매월 2번째 특정요일간격 시간인덱스 생성\n",
    "pd.date_range('2100-01-01', periods=10, freq='WOM-3FRI')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|  **함수 설정** | **시작날짜** | **근무일반영** |\n",
    "|:--------------:|:------------:|:--------------:|\n",
    "| **freq='M'**   | 월말         | X              |\n",
    "| **freq='MS'**  | 월초         | X              |\n",
    "| **freq='BM'**  | 월말         | O              |\n",
    "| **freq='BMS'** | 월초         | O              |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.963972Z",
     "start_time": "2022-08-07T14:23:16.952970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-01-31', '2100-02-28', '2100-03-31', '2100-04-30',\n",
       "               '2100-05-31', '2100-06-30', '2100-07-31', '2100-08-31',\n",
       "               '2100-09-30', '2100-10-31'],\n",
       "              dtype='datetime64[ns]', freq='M')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# freq 매개변수로 원하는 빈도 설정 가능\n",
    "pd.date_range('2100-01-01', periods=10, freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.978969Z",
     "start_time": "2022-08-07T14:23:16.966980Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-01-01', '2100-02-01', '2100-03-01', '2100-04-01',\n",
       "               '2100-05-01', '2100-06-01', '2100-07-01', '2100-08-01',\n",
       "               '2100-09-01', '2100-10-01'],\n",
       "              dtype='datetime64[ns]', freq='MS')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# freq 매개변수로 원하는 빈도 설정 가능\n",
    "pd.date_range('2100-01-01', periods=10, freq='MS')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|     **함수 설정**    | **시작날짜** | **근무일반영** |\n",
    "|:--------------------:|:------------:|:--------------:|\n",
    "| **freq='Q-(MON)'**   | 월말         | X              |\n",
    "| **freq='QS-(MON)'**  | 월초         | X              |\n",
    "| **freq='BQ-(MON)'**  | 월말         | O              |\n",
    "| **freq='BQS-(MON)'** | 월초         | O              |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:16.994971Z",
     "start_time": "2022-08-07T14:23:16.983974Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-03-31', '2100-06-30', '2100-09-30', '2100-12-31',\n",
       "               '2101-03-31', '2101-06-30', '2101-09-30', '2101-12-31',\n",
       "               '2102-03-31', '2102-06-30'],\n",
       "              dtype='datetime64[ns]', freq='Q-MAR')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# freq 매개변수로 원하는 빈도 설정 가능\n",
    "pd.date_range('2100-01-01', periods=10, freq='Q-Mar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.009969Z",
     "start_time": "2022-08-07T14:23:16.996972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-03-01', '2100-06-01', '2100-09-01', '2100-12-01',\n",
       "               '2101-03-01', '2101-06-01', '2101-09-01', '2101-12-01',\n",
       "               '2102-03-01', '2102-06-01'],\n",
       "              dtype='datetime64[ns]', freq='QS-MAR')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# freq 매개변수로 원하는 빈도 설정 가능\n",
    "pd.date_range('2100-01-01', periods=10, freq='QS-Mar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|     **함수 설정**    | **시작날짜** | **근무일반영** |\n",
    "|:--------------------:|:------------:|:--------------:|\n",
    "| **freq='A-(MON)'**   | 월말         | X              |\n",
    "| **freq='AS-(MON)'**  | 월초         | X              |\n",
    "| **freq='BA-(MON)'**  | 월말         | O              |\n",
    "| **freq='BAS-(MON)'** | 월초         | O              |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.024972Z",
     "start_time": "2022-08-07T14:23:17.011973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-03-31', '2101-03-31', '2102-03-31', '2103-03-31',\n",
       "               '2104-03-31', '2105-03-31', '2106-03-31', '2107-03-31',\n",
       "               '2108-03-31', '2109-03-31'],\n",
       "              dtype='datetime64[ns]', freq='A-MAR')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# freq 매개변수로 원하는 빈도 설정 가능\n",
    "pd.date_range('2100-01-01', periods=10, freq='A-Mar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.039968Z",
     "start_time": "2022-08-07T14:23:17.026975Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2100-03-01', '2101-03-01', '2102-03-01', '2103-03-01',\n",
       "               '2104-03-01', '2105-03-01', '2106-03-01', '2107-03-01',\n",
       "               '2108-03-01', '2109-03-01'],\n",
       "              dtype='datetime64[ns]', freq='AS-MAR')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# freq 매개변수로 원하는 빈도 설정 가능\n",
    "pd.date_range('2100-01-01', periods=10, freq='AS-Mar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터에 시계열인덱스 적용\n",
    "\n",
    "> **\"`생성한 시간인덱스 또는 시간변수`를 `Series/DataFrame의 Index`로 적용\"**\n",
    ">\n",
    ">```python\n",
    "># 생성한 시간인덱스 적용\n",
    "df.index = pd.date_range(...)\n",
    ">\n",
    "># 시간변수 인덱스로 적용\n",
    "df['변수명'] = pd.to_datetime('변수명')\n",
    "df.set_index('변수명', inplace=True)\n",
    ">```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.100972Z",
     "start_time": "2022-08-07T14:23:17.041970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>Area Abbreviation</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Item Code</th>\n",
       "      <th>Item</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Y2004</th>\n",
       "      <th>Y2005</th>\n",
       "      <th>Y2006</th>\n",
       "      <th>Y2007</th>\n",
       "      <th>Y2008</th>\n",
       "      <th>Y2009</th>\n",
       "      <th>Y2010</th>\n",
       "      <th>Y2011</th>\n",
       "      <th>Y2012</th>\n",
       "      <th>Y2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2100-01-01</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>3249.0</td>\n",
       "      <td>3486.0</td>\n",
       "      <td>3704.0</td>\n",
       "      <td>4164.0</td>\n",
       "      <td>4252.0</td>\n",
       "      <td>4538.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>4711.0</td>\n",
       "      <td>4810</td>\n",
       "      <td>4895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-01-02</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2805</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>419.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>425</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-01-03</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2513</td>\n",
       "      <td>Barley and products</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>367</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-01-04</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2513</td>\n",
       "      <td>Barley and products</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>185.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-01-05</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2514</td>\n",
       "      <td>Maize and products</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>120.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>200</td>\n",
       "      <td>200</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-16</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2948</td>\n",
       "      <td>Milk - Excluding Butter</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>373.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>426</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-17</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Fish, Seafood</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-18</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Fish, Seafood</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-19</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Aquatic Products, Other</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-20</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2928</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21477 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Area Abbreviation  Area Code         Area  Item Code  \\\n",
       "2100-01-01                AF          2  Afghanistan       2511   \n",
       "2100-01-02                AF          2  Afghanistan       2805   \n",
       "2100-01-03                AF          2  Afghanistan       2513   \n",
       "2100-01-04                AF          2  Afghanistan       2513   \n",
       "2100-01-05                AF          2  Afghanistan       2514   \n",
       "...                      ...        ...          ...        ...   \n",
       "2158-10-16                ZW        181     Zimbabwe       2948   \n",
       "2158-10-17                ZW        181     Zimbabwe       2960   \n",
       "2158-10-18                ZW        181     Zimbabwe       2960   \n",
       "2158-10-19                ZW        181     Zimbabwe       2961   \n",
       "2158-10-20                ZW        181     Zimbabwe       2928   \n",
       "\n",
       "                                Item  Element Code Element         Unit  \\\n",
       "2100-01-01        Wheat and products          5142    Food  1000 tonnes   \n",
       "2100-01-02  Rice (Milled Equivalent)          5142    Food  1000 tonnes   \n",
       "2100-01-03       Barley and products          5521    Feed  1000 tonnes   \n",
       "2100-01-04       Barley and products          5142    Food  1000 tonnes   \n",
       "2100-01-05        Maize and products          5521    Feed  1000 tonnes   \n",
       "...                              ...           ...     ...          ...   \n",
       "2158-10-16   Milk - Excluding Butter          5142    Food  1000 tonnes   \n",
       "2158-10-17             Fish, Seafood          5521    Feed  1000 tonnes   \n",
       "2158-10-18             Fish, Seafood          5142    Food  1000 tonnes   \n",
       "2158-10-19   Aquatic Products, Other          5142    Food  1000 tonnes   \n",
       "2158-10-20             Miscellaneous          5142    Food  1000 tonnes   \n",
       "\n",
       "            latitude  longitude  ...   Y2004   Y2005   Y2006   Y2007   Y2008  \\\n",
       "2100-01-01     33.94      67.71  ...  3249.0  3486.0  3704.0  4164.0  4252.0   \n",
       "2100-01-02     33.94      67.71  ...   419.0   445.0   546.0   455.0   490.0   \n",
       "2100-01-03     33.94      67.71  ...    58.0   236.0   262.0   263.0   230.0   \n",
       "2100-01-04     33.94      67.71  ...   185.0    43.0    44.0    48.0    62.0   \n",
       "2100-01-05     33.94      67.71  ...   120.0   208.0   233.0   249.0   247.0   \n",
       "...              ...        ...  ...     ...     ...     ...     ...     ...   \n",
       "2158-10-16    -19.02      29.15  ...   373.0   357.0   359.0   356.0   341.0   \n",
       "2158-10-17    -19.02      29.15  ...     5.0     4.0     9.0     6.0     9.0   \n",
       "2158-10-18    -19.02      29.15  ...    18.0    14.0    17.0    14.0    15.0   \n",
       "2158-10-19    -19.02      29.15  ...     0.0     0.0     0.0     0.0     0.0   \n",
       "2158-10-20    -19.02      29.15  ...     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "             Y2009   Y2010   Y2011  Y2012  Y2013  \n",
       "2100-01-01  4538.0  4605.0  4711.0   4810   4895  \n",
       "2100-01-02   415.0   442.0   476.0    425    422  \n",
       "2100-01-03   379.0   315.0   203.0    367    360  \n",
       "2100-01-04    55.0    60.0    72.0     78     89  \n",
       "2100-01-05   195.0   178.0   191.0    200    200  \n",
       "...            ...     ...     ...    ...    ...  \n",
       "2158-10-16   385.0   418.0   457.0    426    451  \n",
       "2158-10-17     5.0    15.0    15.0     15     15  \n",
       "2158-10-18    18.0    29.0    40.0     40     40  \n",
       "2158-10-19     0.0     0.0     0.0      0      0  \n",
       "2158-10-20     0.0     0.0     0.0      0      0  \n",
       "\n",
       "[21477 rows x 24 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 생성한 시간정보로 인덱스 변경\n",
    "df_test = df.copy()\n",
    "df_test.index = pd.date_range('2100-01-01', periods=df.shape[0])\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.236970Z",
     "start_time": "2022-08-07T14:23:17.102972Z"
    }
   },
   "outputs": [
    {
     "data": {
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       " '2100-12-18',\n",
       " '2100-12-19',\n",
       " '2100-12-20',\n",
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       " '2102-07-29',\n",
       " '2102-07-30',\n",
       " '2102-07-31',\n",
       " '2102-08-01',\n",
       " '2102-08-02',\n",
       " '2102-08-03',\n",
       " '2102-08-04',\n",
       " '2102-08-05',\n",
       " '2102-08-06',\n",
       " '2102-08-07',\n",
       " '2102-08-08',\n",
       " '2102-08-09',\n",
       " '2102-08-10',\n",
       " '2102-08-11',\n",
       " '2102-08-12',\n",
       " '2102-08-13',\n",
       " '2102-08-14',\n",
       " '2102-08-15',\n",
       " '2102-08-16',\n",
       " '2102-08-17',\n",
       " '2102-08-18',\n",
       " '2102-08-19',\n",
       " '2102-08-20',\n",
       " '2102-08-21',\n",
       " '2102-08-22',\n",
       " '2102-08-23',\n",
       " '2102-08-24',\n",
       " '2102-08-25',\n",
       " '2102-08-26',\n",
       " '2102-08-27',\n",
       " '2102-08-28',\n",
       " '2102-08-29',\n",
       " '2102-08-30',\n",
       " '2102-08-31',\n",
       " '2102-09-01',\n",
       " '2102-09-02',\n",
       " '2102-09-03',\n",
       " '2102-09-04',\n",
       " '2102-09-05',\n",
       " '2102-09-06',\n",
       " '2102-09-07',\n",
       " '2102-09-08',\n",
       " '2102-09-09',\n",
       " '2102-09-10',\n",
       " '2102-09-11',\n",
       " '2102-09-12',\n",
       " '2102-09-13',\n",
       " '2102-09-14',\n",
       " '2102-09-15',\n",
       " '2102-09-16',\n",
       " '2102-09-17',\n",
       " '2102-09-18',\n",
       " '2102-09-19',\n",
       " '2102-09-20',\n",
       " '2102-09-21',\n",
       " '2102-09-22',\n",
       " '2102-09-23',\n",
       " '2102-09-24',\n",
       " '2102-09-25',\n",
       " '2102-09-26',\n",
       " '2102-09-27',\n",
       " ...]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임의 시간정보 데이터 리스트 생성\n",
    "def date_range(start, end, datetime_format=\"%Y-%m-%d\"):\n",
    "    import datetime as dt\n",
    "    start = dt.datetime.strptime(start, datetime_format)\n",
    "    end = dt.datetime.strptime(end, datetime_format)\n",
    "    dates = [(start + dt.timedelta(days=i)).strftime(datetime_format) for i in range((end-start).days+1)]\n",
    "    return dates\n",
    "\n",
    "date_range('2100-01-01', '2158-10-20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.401971Z",
     "start_time": "2022-08-07T14:23:17.238971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>Area Abbreviation</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Item Code</th>\n",
       "      <th>Item</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Y2004</th>\n",
       "      <th>Y2005</th>\n",
       "      <th>Y2006</th>\n",
       "      <th>Y2007</th>\n",
       "      <th>Y2008</th>\n",
       "      <th>Y2009</th>\n",
       "      <th>Y2010</th>\n",
       "      <th>Y2011</th>\n",
       "      <th>Y2012</th>\n",
       "      <th>Y2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2100-01-01</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>3249.0</td>\n",
       "      <td>3486.0</td>\n",
       "      <td>3704.0</td>\n",
       "      <td>4164.0</td>\n",
       "      <td>4252.0</td>\n",
       "      <td>4538.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>4711.0</td>\n",
       "      <td>4810</td>\n",
       "      <td>4895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-01-02</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2805</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>419.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>425</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-01-03</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2513</td>\n",
       "      <td>Barley and products</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>367</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-01-04</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2513</td>\n",
       "      <td>Barley and products</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>185.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-01-05</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2514</td>\n",
       "      <td>Maize and products</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>120.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>200</td>\n",
       "      <td>200</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-16</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2948</td>\n",
       "      <td>Milk - Excluding Butter</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>373.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>426</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-17</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Fish, Seafood</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-18</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Fish, Seafood</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-19</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Aquatic Products, Other</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-20</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2928</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21477 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Area Abbreviation  Area Code         Area  Item Code  \\\n",
       "2100-01-01                AF          2  Afghanistan       2511   \n",
       "2100-01-02                AF          2  Afghanistan       2805   \n",
       "2100-01-03                AF          2  Afghanistan       2513   \n",
       "2100-01-04                AF          2  Afghanistan       2513   \n",
       "2100-01-05                AF          2  Afghanistan       2514   \n",
       "...                      ...        ...          ...        ...   \n",
       "2158-10-16                ZW        181     Zimbabwe       2948   \n",
       "2158-10-17                ZW        181     Zimbabwe       2960   \n",
       "2158-10-18                ZW        181     Zimbabwe       2960   \n",
       "2158-10-19                ZW        181     Zimbabwe       2961   \n",
       "2158-10-20                ZW        181     Zimbabwe       2928   \n",
       "\n",
       "                                Item  Element Code Element         Unit  \\\n",
       "2100-01-01        Wheat and products          5142    Food  1000 tonnes   \n",
       "2100-01-02  Rice (Milled Equivalent)          5142    Food  1000 tonnes   \n",
       "2100-01-03       Barley and products          5521    Feed  1000 tonnes   \n",
       "2100-01-04       Barley and products          5142    Food  1000 tonnes   \n",
       "2100-01-05        Maize and products          5521    Feed  1000 tonnes   \n",
       "...                              ...           ...     ...          ...   \n",
       "2158-10-16   Milk - Excluding Butter          5142    Food  1000 tonnes   \n",
       "2158-10-17             Fish, Seafood          5521    Feed  1000 tonnes   \n",
       "2158-10-18             Fish, Seafood          5142    Food  1000 tonnes   \n",
       "2158-10-19   Aquatic Products, Other          5142    Food  1000 tonnes   \n",
       "2158-10-20             Miscellaneous          5142    Food  1000 tonnes   \n",
       "\n",
       "            latitude  longitude  ...   Y2004   Y2005   Y2006   Y2007   Y2008  \\\n",
       "2100-01-01     33.94      67.71  ...  3249.0  3486.0  3704.0  4164.0  4252.0   \n",
       "2100-01-02     33.94      67.71  ...   419.0   445.0   546.0   455.0   490.0   \n",
       "2100-01-03     33.94      67.71  ...    58.0   236.0   262.0   263.0   230.0   \n",
       "2100-01-04     33.94      67.71  ...   185.0    43.0    44.0    48.0    62.0   \n",
       "2100-01-05     33.94      67.71  ...   120.0   208.0   233.0   249.0   247.0   \n",
       "...              ...        ...  ...     ...     ...     ...     ...     ...   \n",
       "2158-10-16    -19.02      29.15  ...   373.0   357.0   359.0   356.0   341.0   \n",
       "2158-10-17    -19.02      29.15  ...     5.0     4.0     9.0     6.0     9.0   \n",
       "2158-10-18    -19.02      29.15  ...    18.0    14.0    17.0    14.0    15.0   \n",
       "2158-10-19    -19.02      29.15  ...     0.0     0.0     0.0     0.0     0.0   \n",
       "2158-10-20    -19.02      29.15  ...     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "             Y2009   Y2010   Y2011  Y2012  Y2013  \n",
       "2100-01-01  4538.0  4605.0  4711.0   4810   4895  \n",
       "2100-01-02   415.0   442.0   476.0    425    422  \n",
       "2100-01-03   379.0   315.0   203.0    367    360  \n",
       "2100-01-04    55.0    60.0    72.0     78     89  \n",
       "2100-01-05   195.0   178.0   191.0    200    200  \n",
       "...            ...     ...     ...    ...    ...  \n",
       "2158-10-16   385.0   418.0   457.0    426    451  \n",
       "2158-10-17     5.0    15.0    15.0     15     15  \n",
       "2158-10-18    18.0    29.0    40.0     40     40  \n",
       "2158-10-19     0.0     0.0     0.0      0      0  \n",
       "2158-10-20     0.0     0.0     0.0      0      0  \n",
       "\n",
       "[21477 rows x 24 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 생성한 시간정보로 인덱스 변경\n",
    "df_test2 = df.copy()\n",
    "\n",
    "df_test2.index = date_range('2100-01-01', '2158-10-20')\n",
    "df_test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.416981Z",
     "start_time": "2022-08-07T14:23:17.403976Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(DatetimeIndex(['2100-01-01', '2100-01-02', '2100-01-03', '2100-01-04',\n",
       "                '2100-01-05', '2100-01-06', '2100-01-07', '2100-01-08',\n",
       "                '2100-01-09', '2100-01-10',\n",
       "                ...\n",
       "                '2158-10-11', '2158-10-12', '2158-10-13', '2158-10-14',\n",
       "                '2158-10-15', '2158-10-16', '2158-10-17', '2158-10-18',\n",
       "                '2158-10-19', '2158-10-20'],\n",
       "               dtype='datetime64[ns]', length=21477, freq='D'),\n",
       " Index(['2100-01-01', '2100-01-02', '2100-01-03', '2100-01-04', '2100-01-05',\n",
       "        '2100-01-06', '2100-01-07', '2100-01-08', '2100-01-09', '2100-01-10',\n",
       "        ...\n",
       "        '2158-10-11', '2158-10-12', '2158-10-13', '2158-10-14', '2158-10-15',\n",
       "        '2158-10-16', '2158-10-17', '2158-10-18', '2158-10-19', '2158-10-20'],\n",
       "       dtype='object', length=21477))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터별 인덱스 차이 확인\n",
    "df_test.index, df_test2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
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      "text/plain": [
       "           Area Abbreviation  Area Code         Area  Item Code  \\\n",
       "2150-01-01                SR        207     Suriname       2656   \n",
       "2150-01-02                SR        207     Suriname       2657   \n",
       "2150-01-03                SR        207     Suriname       2658   \n",
       "2150-01-04                SR        207     Suriname       2731   \n",
       "2150-01-05                SR        207     Suriname       2732   \n",
       "...                      ...        ...          ...        ...   \n",
       "2150-12-27                CH        211  Switzerland       2574   \n",
       "2150-12-28                CH        211  Switzerland       2575   \n",
       "2150-12-29                CH        211  Switzerland       2576   \n",
       "2150-12-30                CH        211  Switzerland       2577   \n",
       "2150-12-31                CH        211  Switzerland       2578   \n",
       "\n",
       "                            Item  Element Code Element         Unit  latitude  \\\n",
       "2150-01-01                  Beer          5142    Food  1000 tonnes      3.92   \n",
       "2150-01-02  Beverages, Fermented          5142    Food  1000 tonnes      3.92   \n",
       "2150-01-03  Beverages, Alcoholic          5142    Food  1000 tonnes      3.92   \n",
       "2150-01-04           Bovine Meat          5142    Food  1000 tonnes      3.92   \n",
       "2150-01-05    Mutton & Goat Meat          5142    Food  1000 tonnes      3.92   \n",
       "...                          ...           ...     ...          ...       ...   \n",
       "2150-12-27  Rape and Mustard Oil          5142    Food  1000 tonnes     46.82   \n",
       "2150-12-28        Cottonseed Oil          5142    Food  1000 tonnes     46.82   \n",
       "2150-12-29        Palmkernel Oil          5142    Food  1000 tonnes     46.82   \n",
       "2150-12-30              Palm Oil          5142    Food  1000 tonnes     46.82   \n",
       "2150-12-31           Coconut Oil          5142    Food  1000 tonnes     46.82   \n",
       "\n",
       "            longitude  ...  Y2004  Y2005  Y2006  Y2007  Y2008  Y2009  Y2010  \\\n",
       "2150-01-01     -56.03  ...   20.0   22.0   22.0   23.0   24.0   25.0   25.0   \n",
       "2150-01-02     -56.03  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "2150-01-03     -56.03  ...    3.0    3.0    2.0    2.0    2.0    3.0    2.0   \n",
       "2150-01-04     -56.03  ...    2.0    2.0    3.0    4.0    4.0    4.0    6.0   \n",
       "2150-01-05     -56.03  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "...               ...  ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "2150-12-27       8.23  ...   18.0   19.0   19.0   22.0   26.0   32.0   31.0   \n",
       "2150-12-28       8.23  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "2150-12-29       8.23  ...    2.0    2.0    2.0    3.0    3.0    4.0    4.0   \n",
       "2150-12-30       8.23  ...   15.0   16.0   16.0   15.0   15.0   15.0   18.0   \n",
       "2150-12-31       8.23  ...    7.0    8.0    9.0    8.0    9.0    7.0    8.0   \n",
       "\n",
       "            Y2011  Y2012  Y2013  \n",
       "2150-01-01   26.0     26     26  \n",
       "2150-01-02    0.0      0      0  \n",
       "2150-01-03    2.0      2      2  \n",
       "2150-01-04    6.0      6      5  \n",
       "2150-01-05    0.0      0      0  \n",
       "...           ...    ...    ...  \n",
       "2150-12-27   33.0     31     36  \n",
       "2150-12-28    0.0      0      0  \n",
       "2150-12-29    4.0      4      3  \n",
       "2150-12-30   17.0     16     16  \n",
       "2150-12-31    6.0      7      7  \n",
       "\n",
       "[365 rows x 24 columns]"
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-01-05</th>\n",
       "      <td>SR</td>\n",
       "      <td>207</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>2732</td>\n",
       "      <td>Mutton &amp; Goat Meat</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>3.92</td>\n",
       "      <td>-56.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-12-27</th>\n",
       "      <td>CH</td>\n",
       "      <td>211</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2574</td>\n",
       "      <td>Rape and Mustard Oil</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>46.82</td>\n",
       "      <td>8.23</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>31</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-12-28</th>\n",
       "      <td>CH</td>\n",
       "      <td>211</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2575</td>\n",
       "      <td>Cottonseed Oil</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>46.82</td>\n",
       "      <td>8.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-12-29</th>\n",
       "      <td>CH</td>\n",
       "      <td>211</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2576</td>\n",
       "      <td>Palmkernel Oil</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>46.82</td>\n",
       "      <td>8.23</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-12-30</th>\n",
       "      <td>CH</td>\n",
       "      <td>211</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2577</td>\n",
       "      <td>Palm Oil</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>46.82</td>\n",
       "      <td>8.23</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-12-31</th>\n",
       "      <td>CH</td>\n",
       "      <td>211</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2578</td>\n",
       "      <td>Coconut Oil</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>46.82</td>\n",
       "      <td>8.23</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Area Abbreviation  Area Code         Area  Item Code  \\\n",
       "2150-01-01                SR        207     Suriname       2656   \n",
       "2150-01-02                SR        207     Suriname       2657   \n",
       "2150-01-03                SR        207     Suriname       2658   \n",
       "2150-01-04                SR        207     Suriname       2731   \n",
       "2150-01-05                SR        207     Suriname       2732   \n",
       "...                      ...        ...          ...        ...   \n",
       "2150-12-27                CH        211  Switzerland       2574   \n",
       "2150-12-28                CH        211  Switzerland       2575   \n",
       "2150-12-29                CH        211  Switzerland       2576   \n",
       "2150-12-30                CH        211  Switzerland       2577   \n",
       "2150-12-31                CH        211  Switzerland       2578   \n",
       "\n",
       "                            Item  Element Code Element         Unit  latitude  \\\n",
       "2150-01-01                  Beer          5142    Food  1000 tonnes      3.92   \n",
       "2150-01-02  Beverages, Fermented          5142    Food  1000 tonnes      3.92   \n",
       "2150-01-03  Beverages, Alcoholic          5142    Food  1000 tonnes      3.92   \n",
       "2150-01-04           Bovine Meat          5142    Food  1000 tonnes      3.92   \n",
       "2150-01-05    Mutton & Goat Meat          5142    Food  1000 tonnes      3.92   \n",
       "...                          ...           ...     ...          ...       ...   \n",
       "2150-12-27  Rape and Mustard Oil          5142    Food  1000 tonnes     46.82   \n",
       "2150-12-28        Cottonseed Oil          5142    Food  1000 tonnes     46.82   \n",
       "2150-12-29        Palmkernel Oil          5142    Food  1000 tonnes     46.82   \n",
       "2150-12-30              Palm Oil          5142    Food  1000 tonnes     46.82   \n",
       "2150-12-31           Coconut Oil          5142    Food  1000 tonnes     46.82   \n",
       "\n",
       "            longitude  ...  Y2004  Y2005  Y2006  Y2007  Y2008  Y2009  Y2010  \\\n",
       "2150-01-01     -56.03  ...   20.0   22.0   22.0   23.0   24.0   25.0   25.0   \n",
       "2150-01-02     -56.03  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "2150-01-03     -56.03  ...    3.0    3.0    2.0    2.0    2.0    3.0    2.0   \n",
       "2150-01-04     -56.03  ...    2.0    2.0    3.0    4.0    4.0    4.0    6.0   \n",
       "2150-01-05     -56.03  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "...               ...  ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "2150-12-27       8.23  ...   18.0   19.0   19.0   22.0   26.0   32.0   31.0   \n",
       "2150-12-28       8.23  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "2150-12-29       8.23  ...    2.0    2.0    2.0    3.0    3.0    4.0    4.0   \n",
       "2150-12-30       8.23  ...   15.0   16.0   16.0   15.0   15.0   15.0   18.0   \n",
       "2150-12-31       8.23  ...    7.0    8.0    9.0    8.0    9.0    7.0    8.0   \n",
       "\n",
       "            Y2011  Y2012  Y2013  \n",
       "2150-01-01   26.0     26     26  \n",
       "2150-01-02    0.0      0      0  \n",
       "2150-01-03    2.0      2      2  \n",
       "2150-01-04    6.0      6      5  \n",
       "2150-01-05    0.0      0      0  \n",
       "...           ...    ...    ...  \n",
       "2150-12-27   33.0     31     36  \n",
       "2150-12-28    0.0      0      0  \n",
       "2150-12-29    4.0      4      3  \n",
       "2150-12-30   17.0     16     16  \n",
       "2150-12-31    6.0      7      7  \n",
       "\n",
       "[365 rows x 24 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 우리 눈에 날짜시간 정보더라도 컴퓨터가 날짜시간 정보로 인식해야\n",
    "display(df_test['2150-01-01':'2150-12-31'], df_test2['2150-01-01':'2150-12-31'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.537969Z",
     "start_time": "2022-08-07T14:23:17.494971Z"
    }
   },
   "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>Area Abbreviation</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Item Code</th>\n",
       "      <th>Item</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Y2004</th>\n",
       "      <th>Y2005</th>\n",
       "      <th>Y2006</th>\n",
       "      <th>Y2007</th>\n",
       "      <th>Y2008</th>\n",
       "      <th>Y2009</th>\n",
       "      <th>Y2010</th>\n",
       "      <th>Y2011</th>\n",
       "      <th>Y2012</th>\n",
       "      <th>Y2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2150-01-01</th>\n",
       "      <td>SR</td>\n",
       "      <td>207</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>2656</td>\n",
       "      <td>Beer</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>3.92</td>\n",
       "      <td>-56.03</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-01-02</th>\n",
       "      <td>SR</td>\n",
       "      <td>207</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>2657</td>\n",
       "      <td>Beverages, Fermented</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>3.92</td>\n",
       "      <td>-56.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-01-03</th>\n",
       "      <td>SR</td>\n",
       "      <td>207</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>2658</td>\n",
       "      <td>Beverages, Alcoholic</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>3.92</td>\n",
       "      <td>-56.03</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-01-04</th>\n",
       "      <td>SR</td>\n",
       "      <td>207</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>2731</td>\n",
       "      <td>Bovine Meat</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>3.92</td>\n",
       "      <td>-56.03</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-01-05</th>\n",
       "      <td>SR</td>\n",
       "      <td>207</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>2732</td>\n",
       "      <td>Mutton &amp; Goat Meat</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>3.92</td>\n",
       "      <td>-56.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-12-27</th>\n",
       "      <td>CH</td>\n",
       "      <td>211</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2574</td>\n",
       "      <td>Rape and Mustard Oil</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>46.82</td>\n",
       "      <td>8.23</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>31</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-12-28</th>\n",
       "      <td>CH</td>\n",
       "      <td>211</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2575</td>\n",
       "      <td>Cottonseed Oil</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>46.82</td>\n",
       "      <td>8.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-12-29</th>\n",
       "      <td>CH</td>\n",
       "      <td>211</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2576</td>\n",
       "      <td>Palmkernel Oil</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>46.82</td>\n",
       "      <td>8.23</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-12-30</th>\n",
       "      <td>CH</td>\n",
       "      <td>211</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2577</td>\n",
       "      <td>Palm Oil</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>46.82</td>\n",
       "      <td>8.23</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150-12-31</th>\n",
       "      <td>CH</td>\n",
       "      <td>211</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2578</td>\n",
       "      <td>Coconut Oil</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>46.82</td>\n",
       "      <td>8.23</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Area Abbreviation  Area Code         Area  Item Code  \\\n",
       "2150-01-01                SR        207     Suriname       2656   \n",
       "2150-01-02                SR        207     Suriname       2657   \n",
       "2150-01-03                SR        207     Suriname       2658   \n",
       "2150-01-04                SR        207     Suriname       2731   \n",
       "2150-01-05                SR        207     Suriname       2732   \n",
       "...                      ...        ...          ...        ...   \n",
       "2150-12-27                CH        211  Switzerland       2574   \n",
       "2150-12-28                CH        211  Switzerland       2575   \n",
       "2150-12-29                CH        211  Switzerland       2576   \n",
       "2150-12-30                CH        211  Switzerland       2577   \n",
       "2150-12-31                CH        211  Switzerland       2578   \n",
       "\n",
       "                            Item  Element Code Element         Unit  latitude  \\\n",
       "2150-01-01                  Beer          5142    Food  1000 tonnes      3.92   \n",
       "2150-01-02  Beverages, Fermented          5142    Food  1000 tonnes      3.92   \n",
       "2150-01-03  Beverages, Alcoholic          5142    Food  1000 tonnes      3.92   \n",
       "2150-01-04           Bovine Meat          5142    Food  1000 tonnes      3.92   \n",
       "2150-01-05    Mutton & Goat Meat          5142    Food  1000 tonnes      3.92   \n",
       "...                          ...           ...     ...          ...       ...   \n",
       "2150-12-27  Rape and Mustard Oil          5142    Food  1000 tonnes     46.82   \n",
       "2150-12-28        Cottonseed Oil          5142    Food  1000 tonnes     46.82   \n",
       "2150-12-29        Palmkernel Oil          5142    Food  1000 tonnes     46.82   \n",
       "2150-12-30              Palm Oil          5142    Food  1000 tonnes     46.82   \n",
       "2150-12-31           Coconut Oil          5142    Food  1000 tonnes     46.82   \n",
       "\n",
       "            longitude  ...  Y2004  Y2005  Y2006  Y2007  Y2008  Y2009  Y2010  \\\n",
       "2150-01-01     -56.03  ...   20.0   22.0   22.0   23.0   24.0   25.0   25.0   \n",
       "2150-01-02     -56.03  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "2150-01-03     -56.03  ...    3.0    3.0    2.0    2.0    2.0    3.0    2.0   \n",
       "2150-01-04     -56.03  ...    2.0    2.0    3.0    4.0    4.0    4.0    6.0   \n",
       "2150-01-05     -56.03  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "...               ...  ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "2150-12-27       8.23  ...   18.0   19.0   19.0   22.0   26.0   32.0   31.0   \n",
       "2150-12-28       8.23  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "2150-12-29       8.23  ...    2.0    2.0    2.0    3.0    3.0    4.0    4.0   \n",
       "2150-12-30       8.23  ...   15.0   16.0   16.0   15.0   15.0   15.0   18.0   \n",
       "2150-12-31       8.23  ...    7.0    8.0    9.0    8.0    9.0    7.0    8.0   \n",
       "\n",
       "            Y2011  Y2012  Y2013  \n",
       "2150-01-01   26.0     26     26  \n",
       "2150-01-02    0.0      0      0  \n",
       "2150-01-03    2.0      2      2  \n",
       "2150-01-04    6.0      6      5  \n",
       "2150-01-05    0.0      0      0  \n",
       "...           ...    ...    ...  \n",
       "2150-12-27   33.0     31     36  \n",
       "2150-12-28    0.0      0      0  \n",
       "2150-12-29    4.0      4      3  \n",
       "2150-12-30   17.0     16     16  \n",
       "2150-12-31    6.0      7      7  \n",
       "\n",
       "[365 rows x 24 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 우리 눈에 날짜시간 정보더라도 컴퓨터가 날짜시간 정보로 인식해야\n",
    "df_test.loc['2150']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.553969Z",
     "start_time": "2022-08-07T14:23:17.539971Z"
    }
   },
   "outputs": [],
   "source": [
    "# 우리 눈에 날짜시간 정보더라도 컴퓨터가 날짜시간 정보로 인식해야\n",
    "# df_test2.loc['2150']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.583987Z",
     "start_time": "2022-08-07T14:23:17.555973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(index                datetime64[ns]\n",
       " Area Abbreviation            object\n",
       " Area Code                     int64\n",
       " Area                         object\n",
       " Item Code                     int64\n",
       " Item                         object\n",
       " Element Code                  int64\n",
       " Element                      object\n",
       " Unit                         object\n",
       " latitude                    float64\n",
       " longitude                   float64\n",
       " Y2000                       float64\n",
       " Y2001                       float64\n",
       " Y2002                       float64\n",
       " Y2003                       float64\n",
       " Y2004                       float64\n",
       " Y2005                       float64\n",
       " Y2006                       float64\n",
       " Y2007                       float64\n",
       " Y2008                       float64\n",
       " Y2009                       float64\n",
       " Y2010                       float64\n",
       " Y2011                       float64\n",
       " Y2012                         int64\n",
       " Y2013                         int64\n",
       " dtype: object,\n",
       " index                 object\n",
       " Area Abbreviation     object\n",
       " Area Code              int64\n",
       " Area                  object\n",
       " Item Code              int64\n",
       " Item                  object\n",
       " Element Code           int64\n",
       " Element               object\n",
       " Unit                  object\n",
       " latitude             float64\n",
       " longitude            float64\n",
       " Y2000                float64\n",
       " Y2001                float64\n",
       " Y2002                float64\n",
       " Y2003                float64\n",
       " Y2004                float64\n",
       " Y2005                float64\n",
       " Y2006                float64\n",
       " Y2007                float64\n",
       " Y2008                float64\n",
       " Y2009                float64\n",
       " Y2010                float64\n",
       " Y2011                float64\n",
       " Y2012                  int64\n",
       " Y2013                  int64\n",
       " dtype: object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 인덱스의 형식 차이 확인\n",
    "df_test.reset_index().dtypes, df_test2.reset_index().dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.752971Z",
     "start_time": "2022-08-07T14:23:17.587972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>Area Abbreviation</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Item Code</th>\n",
       "      <th>Item</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Y2005</th>\n",
       "      <th>Y2006</th>\n",
       "      <th>Y2007</th>\n",
       "      <th>Y2008</th>\n",
       "      <th>Y2009</th>\n",
       "      <th>Y2010</th>\n",
       "      <th>Y2011</th>\n",
       "      <th>Y2012</th>\n",
       "      <th>Y2013</th>\n",
       "      <th>DateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>3486.0</td>\n",
       "      <td>3704.0</td>\n",
       "      <td>4164.0</td>\n",
       "      <td>4252.0</td>\n",
       "      <td>4538.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>4711.0</td>\n",
       "      <td>4810</td>\n",
       "      <td>4895</td>\n",
       "      <td>2100-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2805</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>445.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>425</td>\n",
       "      <td>422</td>\n",
       "      <td>2100-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2513</td>\n",
       "      <td>Barley and products</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>236.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>367</td>\n",
       "      <td>360</td>\n",
       "      <td>2100-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2513</td>\n",
       "      <td>Barley and products</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "      <td>2100-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2514</td>\n",
       "      <td>Maize and products</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>208.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>2100-01-05</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21472</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2948</td>\n",
       "      <td>Milk - Excluding Butter</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>357.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>426</td>\n",
       "      <td>451</td>\n",
       "      <td>2158-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21473</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Fish, Seafood</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>2158-10-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21474</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Fish, Seafood</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>2158-10-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21475</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Aquatic Products, Other</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2158-10-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21476</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2928</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2158-10-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21477 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Area Abbreviation  Area Code         Area  Item Code  \\\n",
       "0                    AF          2  Afghanistan       2511   \n",
       "1                    AF          2  Afghanistan       2805   \n",
       "2                    AF          2  Afghanistan       2513   \n",
       "3                    AF          2  Afghanistan       2513   \n",
       "4                    AF          2  Afghanistan       2514   \n",
       "...                 ...        ...          ...        ...   \n",
       "21472                ZW        181     Zimbabwe       2948   \n",
       "21473                ZW        181     Zimbabwe       2960   \n",
       "21474                ZW        181     Zimbabwe       2960   \n",
       "21475                ZW        181     Zimbabwe       2961   \n",
       "21476                ZW        181     Zimbabwe       2928   \n",
       "\n",
       "                           Item  Element Code Element         Unit  latitude  \\\n",
       "0            Wheat and products          5142    Food  1000 tonnes     33.94   \n",
       "1      Rice (Milled Equivalent)          5142    Food  1000 tonnes     33.94   \n",
       "2           Barley and products          5521    Feed  1000 tonnes     33.94   \n",
       "3           Barley and products          5142    Food  1000 tonnes     33.94   \n",
       "4            Maize and products          5521    Feed  1000 tonnes     33.94   \n",
       "...                         ...           ...     ...          ...       ...   \n",
       "21472   Milk - Excluding Butter          5142    Food  1000 tonnes    -19.02   \n",
       "21473             Fish, Seafood          5521    Feed  1000 tonnes    -19.02   \n",
       "21474             Fish, Seafood          5142    Food  1000 tonnes    -19.02   \n",
       "21475   Aquatic Products, Other          5142    Food  1000 tonnes    -19.02   \n",
       "21476             Miscellaneous          5142    Food  1000 tonnes    -19.02   \n",
       "\n",
       "       longitude  ...   Y2005   Y2006   Y2007   Y2008   Y2009   Y2010   Y2011  \\\n",
       "0          67.71  ...  3486.0  3704.0  4164.0  4252.0  4538.0  4605.0  4711.0   \n",
       "1          67.71  ...   445.0   546.0   455.0   490.0   415.0   442.0   476.0   \n",
       "2          67.71  ...   236.0   262.0   263.0   230.0   379.0   315.0   203.0   \n",
       "3          67.71  ...    43.0    44.0    48.0    62.0    55.0    60.0    72.0   \n",
       "4          67.71  ...   208.0   233.0   249.0   247.0   195.0   178.0   191.0   \n",
       "...          ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "21472      29.15  ...   357.0   359.0   356.0   341.0   385.0   418.0   457.0   \n",
       "21473      29.15  ...     4.0     9.0     6.0     9.0     5.0    15.0    15.0   \n",
       "21474      29.15  ...    14.0    17.0    14.0    15.0    18.0    29.0    40.0   \n",
       "21475      29.15  ...     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "21476      29.15  ...     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "       Y2012  Y2013    DateTime  \n",
       "0       4810   4895  2100-01-01  \n",
       "1        425    422  2100-01-02  \n",
       "2        367    360  2100-01-03  \n",
       "3         78     89  2100-01-04  \n",
       "4        200    200  2100-01-05  \n",
       "...      ...    ...         ...  \n",
       "21472    426    451  2158-10-16  \n",
       "21473     15     15  2158-10-17  \n",
       "21474     40     40  2158-10-18  \n",
       "21475      0      0  2158-10-19  \n",
       "21476      0      0  2158-10-20  \n",
       "\n",
       "[21477 rows x 25 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 문자형태인 특정 변수의 시간정보를 DatetimeIndex로 반영\n",
    "# 문자형태인 특정 변수 확인\n",
    "df_test2 = df.copy()\n",
    "df_test2['DateTime'] = date_range('2100-01-01', '2158-10-20')\n",
    "df_test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.767975Z",
     "start_time": "2022-08-07T14:23:17.755971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Area Abbreviation     object\n",
       "Area Code              int64\n",
       "Area                  object\n",
       "Item Code              int64\n",
       "Item                  object\n",
       "Element Code           int64\n",
       "Element               object\n",
       "Unit                  object\n",
       "latitude             float64\n",
       "longitude            float64\n",
       "Y2000                float64\n",
       "Y2001                float64\n",
       "Y2002                float64\n",
       "Y2003                float64\n",
       "Y2004                float64\n",
       "Y2005                float64\n",
       "Y2006                float64\n",
       "Y2007                float64\n",
       "Y2008                float64\n",
       "Y2009                float64\n",
       "Y2010                float64\n",
       "Y2011                float64\n",
       "Y2012                  int64\n",
       "Y2013                  int64\n",
       "DateTime              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 문자형태인 특정 변수 확인\n",
    "df_test2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.798975Z",
     "start_time": "2022-08-07T14:23:17.770972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Area Abbreviation            object\n",
       "Area Code                     int64\n",
       "Area                         object\n",
       "Item Code                     int64\n",
       "Item                         object\n",
       "Element Code                  int64\n",
       "Element                      object\n",
       "Unit                         object\n",
       "latitude                    float64\n",
       "longitude                   float64\n",
       "Y2000                       float64\n",
       "Y2001                       float64\n",
       "Y2002                       float64\n",
       "Y2003                       float64\n",
       "Y2004                       float64\n",
       "Y2005                       float64\n",
       "Y2006                       float64\n",
       "Y2007                       float64\n",
       "Y2008                       float64\n",
       "Y2009                       float64\n",
       "Y2010                       float64\n",
       "Y2011                       float64\n",
       "Y2012                         int64\n",
       "Y2013                         int64\n",
       "DateTime             datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 문자형태 시간정보를 datetime 형식으로 변환\n",
    "df_test2['DateTime'] = pd.to_datetime(df_test2['DateTime'])\n",
    "df_test2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.846970Z",
     "start_time": "2022-08-07T14:23:17.801972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>Area Abbreviation</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Item Code</th>\n",
       "      <th>Item</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Y2004</th>\n",
       "      <th>Y2005</th>\n",
       "      <th>Y2006</th>\n",
       "      <th>Y2007</th>\n",
       "      <th>Y2008</th>\n",
       "      <th>Y2009</th>\n",
       "      <th>Y2010</th>\n",
       "      <th>Y2011</th>\n",
       "      <th>Y2012</th>\n",
       "      <th>Y2013</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</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",
       "      <th></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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2100-01-01</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>3249.0</td>\n",
       "      <td>3486.0</td>\n",
       "      <td>3704.0</td>\n",
       "      <td>4164.0</td>\n",
       "      <td>4252.0</td>\n",
       "      <td>4538.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>4711.0</td>\n",
       "      <td>4810</td>\n",
       "      <td>4895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-01-02</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2805</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>419.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>425</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-01-03</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2513</td>\n",
       "      <td>Barley and products</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>367</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-01-04</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2513</td>\n",
       "      <td>Barley and products</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>185.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>78</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100-01-05</th>\n",
       "      <td>AF</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2514</td>\n",
       "      <td>Maize and products</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>33.94</td>\n",
       "      <td>67.71</td>\n",
       "      <td>...</td>\n",
       "      <td>120.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>200</td>\n",
       "      <td>200</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-16</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2948</td>\n",
       "      <td>Milk - Excluding Butter</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>373.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>426</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-17</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Fish, Seafood</td>\n",
       "      <td>5521</td>\n",
       "      <td>Feed</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-18</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2960</td>\n",
       "      <td>Fish, Seafood</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-19</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2961</td>\n",
       "      <td>Aquatic Products, Other</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158-10-20</th>\n",
       "      <td>ZW</td>\n",
       "      <td>181</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2928</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>5142</td>\n",
       "      <td>Food</td>\n",
       "      <td>1000 tonnes</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>29.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21477 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Area Abbreviation  Area Code         Area  Item Code  \\\n",
       "DateTime                                                          \n",
       "2100-01-01                AF          2  Afghanistan       2511   \n",
       "2100-01-02                AF          2  Afghanistan       2805   \n",
       "2100-01-03                AF          2  Afghanistan       2513   \n",
       "2100-01-04                AF          2  Afghanistan       2513   \n",
       "2100-01-05                AF          2  Afghanistan       2514   \n",
       "...                      ...        ...          ...        ...   \n",
       "2158-10-16                ZW        181     Zimbabwe       2948   \n",
       "2158-10-17                ZW        181     Zimbabwe       2960   \n",
       "2158-10-18                ZW        181     Zimbabwe       2960   \n",
       "2158-10-19                ZW        181     Zimbabwe       2961   \n",
       "2158-10-20                ZW        181     Zimbabwe       2928   \n",
       "\n",
       "                                Item  Element Code Element         Unit  \\\n",
       "DateTime                                                                  \n",
       "2100-01-01        Wheat and products          5142    Food  1000 tonnes   \n",
       "2100-01-02  Rice (Milled Equivalent)          5142    Food  1000 tonnes   \n",
       "2100-01-03       Barley and products          5521    Feed  1000 tonnes   \n",
       "2100-01-04       Barley and products          5142    Food  1000 tonnes   \n",
       "2100-01-05        Maize and products          5521    Feed  1000 tonnes   \n",
       "...                              ...           ...     ...          ...   \n",
       "2158-10-16   Milk - Excluding Butter          5142    Food  1000 tonnes   \n",
       "2158-10-17             Fish, Seafood          5521    Feed  1000 tonnes   \n",
       "2158-10-18             Fish, Seafood          5142    Food  1000 tonnes   \n",
       "2158-10-19   Aquatic Products, Other          5142    Food  1000 tonnes   \n",
       "2158-10-20             Miscellaneous          5142    Food  1000 tonnes   \n",
       "\n",
       "            latitude  longitude  ...   Y2004   Y2005   Y2006   Y2007   Y2008  \\\n",
       "DateTime                         ...                                           \n",
       "2100-01-01     33.94      67.71  ...  3249.0  3486.0  3704.0  4164.0  4252.0   \n",
       "2100-01-02     33.94      67.71  ...   419.0   445.0   546.0   455.0   490.0   \n",
       "2100-01-03     33.94      67.71  ...    58.0   236.0   262.0   263.0   230.0   \n",
       "2100-01-04     33.94      67.71  ...   185.0    43.0    44.0    48.0    62.0   \n",
       "2100-01-05     33.94      67.71  ...   120.0   208.0   233.0   249.0   247.0   \n",
       "...              ...        ...  ...     ...     ...     ...     ...     ...   \n",
       "2158-10-16    -19.02      29.15  ...   373.0   357.0   359.0   356.0   341.0   \n",
       "2158-10-17    -19.02      29.15  ...     5.0     4.0     9.0     6.0     9.0   \n",
       "2158-10-18    -19.02      29.15  ...    18.0    14.0    17.0    14.0    15.0   \n",
       "2158-10-19    -19.02      29.15  ...     0.0     0.0     0.0     0.0     0.0   \n",
       "2158-10-20    -19.02      29.15  ...     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "             Y2009   Y2010   Y2011  Y2012  Y2013  \n",
       "DateTime                                          \n",
       "2100-01-01  4538.0  4605.0  4711.0   4810   4895  \n",
       "2100-01-02   415.0   442.0   476.0    425    422  \n",
       "2100-01-03   379.0   315.0   203.0    367    360  \n",
       "2100-01-04    55.0    60.0    72.0     78     89  \n",
       "2100-01-05   195.0   178.0   191.0    200    200  \n",
       "...            ...     ...     ...    ...    ...  \n",
       "2158-10-16   385.0   418.0   457.0    426    451  \n",
       "2158-10-17     5.0    15.0    15.0     15     15  \n",
       "2158-10-18    18.0    29.0    40.0     40     40  \n",
       "2158-10-19     0.0     0.0     0.0      0      0  \n",
       "2158-10-20     0.0     0.0     0.0      0      0  \n",
       "\n",
       "[21477 rows x 24 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 날짜 변수를 인덱스로 반영\n",
    "df_test2.set_index('DateTime', inplace=True)\n",
    "df_test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.862969Z",
     "start_time": "2022-08-07T14:23:17.850976Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(DatetimeIndex(['2100-01-01', '2100-01-02', '2100-01-03', '2100-01-04',\n",
       "                '2100-01-05', '2100-01-06', '2100-01-07', '2100-01-08',\n",
       "                '2100-01-09', '2100-01-10',\n",
       "                ...\n",
       "                '2158-10-11', '2158-10-12', '2158-10-13', '2158-10-14',\n",
       "                '2158-10-15', '2158-10-16', '2158-10-17', '2158-10-18',\n",
       "                '2158-10-19', '2158-10-20'],\n",
       "               dtype='datetime64[ns]', name='DateTime', length=21477, freq=None),\n",
       " DatetimeIndex(['2100-01-01', '2100-01-02', '2100-01-03', '2100-01-04',\n",
       "                '2100-01-05', '2100-01-06', '2100-01-07', '2100-01-08',\n",
       "                '2100-01-09', '2100-01-10',\n",
       "                ...\n",
       "                '2158-10-11', '2158-10-12', '2158-10-13', '2158-10-14',\n",
       "                '2158-10-15', '2158-10-16', '2158-10-17', '2158-10-18',\n",
       "                '2158-10-19', '2158-10-20'],\n",
       "               dtype='datetime64[ns]', length=21477, freq='D'))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 날짜 변수를 인덱스로 반영 후 형식 확인\n",
    "df_test2.index, df_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:17.892971Z",
     "start_time": "2022-08-07T14:23:17.865972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(DatetimeIndex(['2100-01-01', '2100-01-02', '2100-01-03', '2100-01-04',\n",
       "                '2100-01-05', '2100-01-06', '2100-01-07', '2100-01-08',\n",
       "                '2100-01-09', '2100-01-10',\n",
       "                ...\n",
       "                '2158-10-11', '2158-10-12', '2158-10-13', '2158-10-14',\n",
       "                '2158-10-15', '2158-10-16', '2158-10-17', '2158-10-18',\n",
       "                '2158-10-19', '2158-10-20'],\n",
       "               dtype='datetime64[ns]', name='DateTime', length=21477, freq='D'),\n",
       " DatetimeIndex(['2100-01-01', '2100-01-02', '2100-01-03', '2100-01-04',\n",
       "                '2100-01-05', '2100-01-06', '2100-01-07', '2100-01-08',\n",
       "                '2100-01-09', '2100-01-10',\n",
       "                ...\n",
       "                '2158-10-11', '2158-10-12', '2158-10-13', '2158-10-14',\n",
       "                '2158-10-15', '2158-10-16', '2158-10-17', '2158-10-18',\n",
       "                '2158-10-19', '2158-10-20'],\n",
       "               dtype='datetime64[ns]', length=21477, freq='D'))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간정보의 빈도(Frequency)도 우리 눈으로 아는 것과 컴퓨터가 아는 것은 다름\n",
    "# 컴퓨터에게 사용하는 데이터의 '시간단위(Frequency)' 제시 필요\n",
    "df_test2 = df_test2.asfreq('D')\n",
    "df_test2.index, df_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.278971Z",
     "start_time": "2022-08-07T14:23:17.894970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 간단한 생산량 시계열 시각화\n",
    "# X축이 생성한 시간정보 인덱스로 표시\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df_test2['Y2013'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터의 시계열인덱스 중복처리\n",
    "\n",
    "> **\"현실 데이터에선 `시간정보가 중복되는 오류` 존재 할 수 있으며, `중복되지 않은 값`들로 `Groupby 하여 값선택` 필요\"**\n",
    ">\n",
    ">```python\n",
    "># 중복 인덱스 존재 확인\n",
    "df.index.is_unique\n",
    ">\n",
    "># 인덱스 중복시 첫번째 값으로 선택\n",
    "df.groupby(level=0).first()\n",
    ">\n",
    "># groupby로 여러가지 통계량 확인 및 최종 값선택\n",
    "df.groupby(level=0).agg(['first', 'last', 'min', 'max', 'mean'])\n",
    ">```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.293969Z",
     "start_time": "2022-08-07T14:23:19.280972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2050-01-01', '2050-01-02', '2050-01-03', '2050-01-04',\n",
       "               '2050-01-05', '2050-01-06', '2050-01-07', '2050-01-08',\n",
       "               '2050-01-09', '2050-01-10', '2050-01-03', '2050-01-07'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임의 중복 인덱스 생성\n",
    "dt_index = pd.date_range('2050-01-01', periods=10)\n",
    "dt_index = dt_index.append(pd.DatetimeIndex(['2050-01-03', '2050-01-07']))\n",
    "dt_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.308969Z",
     "start_time": "2022-08-07T14:23:19.295973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01     0\n",
       "2050-01-02     1\n",
       "2050-01-03     2\n",
       "2050-01-04     3\n",
       "2050-01-05     4\n",
       "2050-01-06     5\n",
       "2050-01-07     6\n",
       "2050-01-08     7\n",
       "2050-01-09     8\n",
       "2050-01-10     9\n",
       "2050-01-03    10\n",
       "2050-01-07    11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 중복 인덱스 사용 데이터 생성\n",
    "df_test = pd.Series(range(len(dt_index)), index=dt_index)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.323971Z",
     "start_time": "2022-08-07T14:23:19.310973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 중복 인덱스 존재 여부 확인\n",
    "df_test.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.338972Z",
     "start_time": "2022-08-07T14:23:19.325970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 중복 인덱스 존재 여부 확인\n",
    "df_test.index.nunique() == len(dt_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.353969Z",
     "start_time": "2022-08-07T14:23:19.340971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01    1\n",
       "2050-01-02    1\n",
       "2050-01-03    2\n",
       "2050-01-04    1\n",
       "2050-01-05    1\n",
       "2050-01-06    1\n",
       "2050-01-07    2\n",
       "2050-01-08    1\n",
       "2050-01-09    1\n",
       "2050-01-10    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 중복 인덱스 존재 여부 확인\n",
    "df_test.groupby(level=0).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.368977Z",
     "start_time": "2022-08-07T14:23:19.355972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01    0.0\n",
       "2050-01-02    1.0\n",
       "2050-01-03    6.0\n",
       "2050-01-04    3.0\n",
       "2050-01-05    4.0\n",
       "2050-01-06    5.0\n",
       "2050-01-07    8.5\n",
       "2050-01-08    7.0\n",
       "2050-01-09    8.0\n",
       "2050-01-10    9.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 중복 인덱스의 값들을 평균하여 사용\n",
    "df_test.groupby(level=0).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.383975Z",
     "start_time": "2022-08-07T14:23:19.370973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01    0\n",
       "2050-01-02    1\n",
       "2050-01-03    2\n",
       "2050-01-04    3\n",
       "2050-01-05    4\n",
       "2050-01-06    5\n",
       "2050-01-07    6\n",
       "2050-01-08    7\n",
       "2050-01-09    8\n",
       "2050-01-10    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 중복 인덱스의 값들 중 첫번째 등장했던 값 사용\n",
    "df_test.groupby(level=0).first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.398971Z",
     "start_time": "2022-08-07T14:23:19.387972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01     0\n",
       "2050-01-02     1\n",
       "2050-01-03    10\n",
       "2050-01-04     3\n",
       "2050-01-05     4\n",
       "2050-01-06     5\n",
       "2050-01-07    11\n",
       "2050-01-08     7\n",
       "2050-01-09     8\n",
       "2050-01-10     9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 중복 인덱스의 값들 중 마자막에 등장했던 값 사용\n",
    "df_test.groupby(level=0).last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.413973Z",
     "start_time": "2022-08-07T14:23:19.401983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-02</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-03</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-04</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-05</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-06</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-07</th>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-08</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-09</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-10</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            first  last  min  max  mean\n",
       "2050-01-01      0     0    0    0   0.0\n",
       "2050-01-02      1     1    1    1   1.0\n",
       "2050-01-03      2    10    2   10   6.0\n",
       "2050-01-04      3     3    3    3   3.0\n",
       "2050-01-05      4     4    4    4   4.0\n",
       "2050-01-06      5     5    5    5   5.0\n",
       "2050-01-07      6    11    6   11   8.5\n",
       "2050-01-08      7     7    7    7   7.0\n",
       "2050-01-09      8     8    8    8   8.0\n",
       "2050-01-10      9     9    9    9   9.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# agg 함수로 다양한 수치 한번에 계산 가능\n",
    "# 값선택 로직 정확성 확인 및 최종 값선택 전처리\n",
    "df_test.groupby(level=0).agg(['first', 'last', 'min', 'max', 'mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터의 시계열인덱스 누락처리\n",
    "\n",
    "> **\"현실 데이터에선 `시간정보가 누락되는 오류` 존재 할 수 있음\"**\n",
    ">\n",
    "> - 금융시장 데이터는 `Business Day`의 데이터만 존재하고 `공휴일은 데이터 없음`\n",
    "> - `일정한 시간주기` 전처리를 위해 `시간빈도(Frequency) 설정`이 필요하고 `resample 함수`로 쉽게 발견 가능\n",
    ">\n",
    ">```python\n",
    "># 빈도설정으로 누락시점을 찾고 데이터 채우기\n",
    "df.asfreq('frequency', method=..., fill_value=...)\n",
    ">\n",
    "># 인덱스 중복시 첫번째 값으로 선택\n",
    "df.groupby(level=0).first()\n",
    ">\n",
    "># groupby로 여러가지 통계량 확인 및 최종 값선택\n",
    "df.groupby(level=0).agg(['first', 'last', 'min', 'max', 'mean'])\n",
    ">```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.429972Z",
     "start_time": "2022-08-07T14:23:19.415979Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2050-01-01', '2050-01-02', '2050-01-04', '2050-01-05',\n",
       "               '2050-01-06', '2050-01-08', '2050-01-09', '2050-01-10'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임의 누락 인덱스 생성\n",
    "dt_index = pd.date_range('2050-01-01', periods=10)\n",
    "dt_index = dt_index.drop(pd.DatetimeIndex(['2050-01-03', '2050-01-07']))\n",
    "dt_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.444971Z",
     "start_time": "2022-08-07T14:23:19.431972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01    0\n",
       "2050-01-02    1\n",
       "2050-01-04    2\n",
       "2050-01-05    3\n",
       "2050-01-06    4\n",
       "2050-01-08    5\n",
       "2050-01-09    6\n",
       "2050-01-10    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 누락 인덱스 사용 데이터 생성\n",
    "df_test = pd.Series(range(len(dt_index)), index=dt_index)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.459971Z",
     "start_time": "2022-08-07T14:23:19.446972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01    0.0\n",
       "2050-01-02    1.0\n",
       "2050-01-03    NaN\n",
       "2050-01-04    2.0\n",
       "2050-01-05    3.0\n",
       "2050-01-06    4.0\n",
       "2050-01-07    NaN\n",
       "2050-01-08    5.0\n",
       "2050-01-09    6.0\n",
       "2050-01-10    7.0\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간빈도만 설정해도 누락된 Timestamp 표시\n",
    "df_test.asfreq('D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.474970Z",
     "start_time": "2022-08-07T14:23:19.461971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01    0\n",
       "2050-01-02    1\n",
       "2050-01-03    1\n",
       "2050-01-04    2\n",
       "2050-01-05    3\n",
       "2050-01-06    4\n",
       "2050-01-07    4\n",
       "2050-01-08    5\n",
       "2050-01-09    6\n",
       "2050-01-10    7\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# method 매개변수로 앞의/뒤의 값으로 누락값을 채울지 결정 \n",
    "df_test.asfreq('D', method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.489969Z",
     "start_time": "2022-08-07T14:23:19.477974Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01     0\n",
       "2050-01-02     1\n",
       "2050-01-03    10\n",
       "2050-01-04     2\n",
       "2050-01-05     3\n",
       "2050-01-06     4\n",
       "2050-01-07    10\n",
       "2050-01-08     5\n",
       "2050-01-09     6\n",
       "2050-01-10     7\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill_value 매개변수로 특정 값으로 누락값 채우기\n",
    "df_test.asfreq('D', fill_value=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.504969Z",
     "start_time": "2022-08-07T14:23:19.491970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01 00:00:00    0.0\n",
       "2050-01-01 01:00:00    NaN\n",
       "2050-01-01 02:00:00    NaN\n",
       "2050-01-01 03:00:00    NaN\n",
       "2050-01-01 04:00:00    NaN\n",
       "                      ... \n",
       "2050-01-09 20:00:00    NaN\n",
       "2050-01-09 21:00:00    NaN\n",
       "2050-01-09 22:00:00    NaN\n",
       "2050-01-09 23:00:00    NaN\n",
       "2050-01-10 00:00:00    7.0\n",
       "Freq: H, Length: 217, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간빈도 변경을 통해 쉽게 다른 차원의 Timestamp 생성 가능\n",
    "df_test.asfreq('H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.519971Z",
     "start_time": "2022-08-07T14:23:19.506971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01 00:00:00    0.0\n",
       "2050-01-01 00:30:00    NaN\n",
       "2050-01-01 01:00:00    NaN\n",
       "2050-01-01 01:30:00    NaN\n",
       "2050-01-01 02:00:00    NaN\n",
       "                      ... \n",
       "2050-01-09 22:00:00    NaN\n",
       "2050-01-09 22:30:00    NaN\n",
       "2050-01-09 23:00:00    NaN\n",
       "2050-01-09 23:30:00    NaN\n",
       "2050-01-10 00:00:00    7.0\n",
       "Freq: 30T, Length: 433, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간빈도 변경을 통해 쉽게 다른 차원의 Timestamp 생성 가능\n",
    "df_test.asfreq('30T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.535972Z",
     "start_time": "2022-08-07T14:23:19.521971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01 00:00:00     0\n",
       "2050-01-01 00:30:00    10\n",
       "2050-01-01 01:00:00    10\n",
       "2050-01-01 01:30:00    10\n",
       "2050-01-01 02:00:00    10\n",
       "                       ..\n",
       "2050-01-09 22:00:00    10\n",
       "2050-01-09 22:30:00    10\n",
       "2050-01-09 23:00:00    10\n",
       "2050-01-09 23:30:00    10\n",
       "2050-01-10 00:00:00     7\n",
       "Freq: 30T, Length: 433, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간빈도 변경을 통해 쉽게 다른 차원의 Timestamp 생성 가능\n",
    "# 결측치 NaN도 처리 가능\n",
    "df_test.asfreq('30T', fill_value=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.550974Z",
     "start_time": "2022-08-07T14:23:19.537971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2050-01-01', '2050-01-02', '2050-01-04', '2050-01-05',\n",
       "               '2050-01-06', '2050-01-08', '2050-01-09', '2050-01-10'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# resample 함수를 통해 asfreq과 마찬가지의 기능 실행 가능\n",
    "df_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.565973Z",
     "start_time": "2022-08-07T14:23:19.553973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01    0.0\n",
       "2050-01-02    1.0\n",
       "2050-01-03    NaN\n",
       "2050-01-04    2.0\n",
       "2050-01-05    3.0\n",
       "2050-01-06    4.0\n",
       "2050-01-07    NaN\n",
       "2050-01-08    5.0\n",
       "2050-01-09    6.0\n",
       "2050-01-10    7.0\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# resample 함수를 통해 asfreq과 마찬가지의 기능 실행 가능\n",
    "# 누락 Timestamp 추적\n",
    "df_test.resample('D').first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.581973Z",
     "start_time": "2022-08-07T14:23:19.568974Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01    0.0\n",
       "2050-01-02    1.0\n",
       "2050-01-03    1.0\n",
       "2050-01-04    2.0\n",
       "2050-01-05    3.0\n",
       "2050-01-06    4.0\n",
       "2050-01-07    4.0\n",
       "2050-01-08    5.0\n",
       "2050-01-09    6.0\n",
       "2050-01-10    7.0\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fillna 함수를 이어서 실행하여 결측치 처리\n",
    "df_test.resample('D').first().fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.597972Z",
     "start_time": "2022-08-07T14:23:19.586974Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01    0.0\n",
       "2050-01-02    1.0\n",
       "2050-01-03    2.0\n",
       "2050-01-04    2.0\n",
       "2050-01-05    3.0\n",
       "2050-01-06    4.0\n",
       "2050-01-07    5.0\n",
       "2050-01-08    5.0\n",
       "2050-01-09    6.0\n",
       "2050-01-10    7.0\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fillna 함수를 이어서 실행하여 결측치 처리\n",
    "df_test.resample('D').first().fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.613971Z",
     "start_time": "2022-08-07T14:23:19.602972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01     0.0\n",
       "2050-01-02     1.0\n",
       "2050-01-03    10.0\n",
       "2050-01-04     2.0\n",
       "2050-01-05     3.0\n",
       "2050-01-06     4.0\n",
       "2050-01-07    10.0\n",
       "2050-01-08     5.0\n",
       "2050-01-09     6.0\n",
       "2050-01-10     7.0\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fillna 함수를 이어서 실행하여 결측치 처리\n",
    "df_test.resample('D').first().fillna(value=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.628969Z",
     "start_time": "2022-08-07T14:23:19.616973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01 00:00:00    0.0\n",
       "2050-01-01 01:00:00    NaN\n",
       "2050-01-01 02:00:00    NaN\n",
       "2050-01-01 03:00:00    NaN\n",
       "2050-01-01 04:00:00    NaN\n",
       "                      ... \n",
       "2050-01-09 20:00:00    NaN\n",
       "2050-01-09 21:00:00    NaN\n",
       "2050-01-09 22:00:00    NaN\n",
       "2050-01-09 23:00:00    NaN\n",
       "2050-01-10 00:00:00    7.0\n",
       "Freq: H, Length: 217, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간빈도 변경을 통해 쉽게 다른 차원의 Timestamp 생성 가능\n",
    "df_test.resample('H').first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.643970Z",
     "start_time": "2022-08-07T14:23:19.631972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01 00:00:00    0.0\n",
       "2050-01-01 00:30:00    NaN\n",
       "2050-01-01 01:00:00    NaN\n",
       "2050-01-01 01:30:00    NaN\n",
       "2050-01-01 02:00:00    NaN\n",
       "                      ... \n",
       "2050-01-09 22:00:00    NaN\n",
       "2050-01-09 22:30:00    NaN\n",
       "2050-01-09 23:00:00    NaN\n",
       "2050-01-09 23:30:00    NaN\n",
       "2050-01-10 00:00:00    7.0\n",
       "Freq: 30T, Length: 433, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간빈도 변경을 통해 쉽게 다른 차원의 Timestamp 생성 가능\n",
    "df_test.resample('30T').first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.658969Z",
     "start_time": "2022-08-07T14:23:19.645970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2050-01-01 00:00:00     0.0\n",
       "2050-01-01 00:30:00    10.0\n",
       "2050-01-01 01:00:00    10.0\n",
       "2050-01-01 01:30:00    10.0\n",
       "2050-01-01 02:00:00    10.0\n",
       "                       ... \n",
       "2050-01-09 22:00:00    10.0\n",
       "2050-01-09 22:30:00    10.0\n",
       "2050-01-09 23:00:00    10.0\n",
       "2050-01-09 23:30:00    10.0\n",
       "2050-01-10 00:00:00     7.0\n",
       "Freq: 30T, Length: 433, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간빈도 변경을 통해 쉽게 다른 차원의 Timestamp 생성 가능\n",
    "# 결측치 NaN도 처리 가능\n",
    "df_test.resample('30T').first().fillna(value=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **빈도별 데이터 집계(Data Aggregation by Frequency)**\n",
    "\n",
    "> **\"특정 `시간단위나 빈도를 변환`하는 과정에서 `데이터 샘플이 증가 또는 감소` 할 수 있는데, 증가/감소에 따른 `적절한 데이터 집계 방법 필요`\"**\n",
    ">\n",
    "> - **Downsampling:** 시간빈도 감소로 `데이터 샘플이 감소`하기 때문에 `데이터 집계/요약 필요`\n",
    "> - **Upsampling:** 시간빈도 증가로 `데이터 샘플이 증가`하기 때문에 `결측치 반영 필요`\n",
    "\n",
    "<center><img src='Image/Advanced/Upsampling-and-downsampling.png' width='300'>(https://sqlrelease.com/python-use-case-resampling-time-series-data-upsampling-and-downsampling-sql-server-2017)</center>\n",
    "\n",
    "<center><img src='Image/Advanced/Downsampling_vs_Upsampling.png' width='600'>(https://rfriend.tistory.com/)</center>\n",
    "\n",
    "---\n",
    "\n",
    "## Downsampling에서 데이터 집계\n",
    "\n",
    "- `resample` 함수는 `asfreq` 보다 더욱 많은 기능이 포함되어 있으며, `데이터 감소에 따른 데이터 집계 연산`을 다양하게 지원"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.673972Z",
     "start_time": "2022-08-07T14:23:19.660972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 02:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 03:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 04:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 05:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 06:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 07:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 08:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 09:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 10:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 11:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 12:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 13:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 14:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 15:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 16:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 17:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 18:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 19:00:00</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [2050-01-01 00:00:00, 2050-01-01 01:00:00, 2050-01-01 02:00:00, 2050-01-01 03:00:00, 2050-01-01 04:00:00, 2050-01-01 05:00:00, 2050-01-01 06:00:00, 2050-01-01 07:00:00, 2050-01-01 08:00:00, 2050-01-01 09:00:00, 2050-01-01 10:00:00, 2050-01-01 11:00:00, 2050-01-01 12:00:00, 2050-01-01 13:00:00, 2050-01-01 14:00:00, 2050-01-01 15:00:00, 2050-01-01 16:00:00, 2050-01-01 17:00:00, 2050-01-01 18:00:00, 2050-01-01 19:00:00]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임의 시계열 인덱스 생성\n",
    "df_test = pd.DataFrame(index=pd.date_range(start='2050-01-01', periods=20, freq='1h'))\n",
    "df_test                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.688971Z",
     "start_time": "2022-08-07T14:23:19.675970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:00:00</th>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 02:00:00</th>\n",
       "      <td>93</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 03:00:00</th>\n",
       "      <td>67</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 04:00:00</th>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 05:00:00</th>\n",
       "      <td>57</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 06:00:00</th>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 07:00:00</th>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 08:00:00</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 09:00:00</th>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 10:00:00</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 11:00:00</th>\n",
       "      <td>88</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 12:00:00</th>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 13:00:00</th>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 14:00:00</th>\n",
       "      <td>78</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 15:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 16:00:00</th>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 17:00:00</th>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 18:00:00</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 19:00:00</th>\n",
       "      <td>94</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00     76       8\n",
       "2050-01-01 01:00:00     27       3\n",
       "2050-01-01 02:00:00     93       5\n",
       "2050-01-01 03:00:00     67       9\n",
       "2050-01-01 04:00:00     96       1\n",
       "2050-01-01 05:00:00     57       8\n",
       "2050-01-01 06:00:00     83       4\n",
       "2050-01-01 07:00:00     42       5\n",
       "2050-01-01 08:00:00     56       7\n",
       "2050-01-01 09:00:00     35       2\n",
       "2050-01-01 10:00:00     93       6\n",
       "2050-01-01 11:00:00     88       7\n",
       "2050-01-01 12:00:00     46       3\n",
       "2050-01-01 13:00:00     90       2\n",
       "2050-01-01 14:00:00     78       9\n",
       "2050-01-01 15:00:00     59       4\n",
       "2050-01-01 16:00:00     65       6\n",
       "2050-01-01 17:00:00     77       1\n",
       "2050-01-01 18:00:00     12       3\n",
       "2050-01-01 19:00:00     94       7"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 가격과 거래량 데이터 생성\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(123)\n",
    "if 'price' not in df.columns:\n",
    "    df_test['price'] = np.random.randint(low=10, high=100, size=20)\n",
    "    df_test['volumn'] = np.random.randint(low=1, high=10, size=20)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.703974Z",
     "start_time": "2022-08-07T14:23:19.690973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.resample.DatetimeIndexResampler object at 0x000001BE33462490>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# resample 함수로 2시간 간격으로 데이터를 추출\n",
    "# 2시간 간격으로 데이터를 groupby 하는 것과 같은 기능\n",
    "df_test.resample('2H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.718979Z",
     "start_time": "2022-08-07T14:23:19.706971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 02:00:00</th>\n",
       "      <td>93</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 04:00:00</th>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 06:00:00</th>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 08:00:00</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 10:00:00</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 12:00:00</th>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 14:00:00</th>\n",
       "      <td>78</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 16:00:00</th>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 18:00:00</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00     76       8\n",
       "2050-01-01 02:00:00     93       5\n",
       "2050-01-01 04:00:00     96       1\n",
       "2050-01-01 06:00:00     83       4\n",
       "2050-01-01 08:00:00     56       7\n",
       "2050-01-01 10:00:00     93       6\n",
       "2050-01-01 12:00:00     46       3\n",
       "2050-01-01 14:00:00     78       9\n",
       "2050-01-01 16:00:00     65       6\n",
       "2050-01-01 18:00:00     12       3"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2시간 간격으로 groupby한 후 첫번째 값들을 추출\n",
    "df_test.resample('2H').first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.734981Z",
     "start_time": "2022-08-07T14:23:19.722973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 02:00:00</th>\n",
       "      <td>67</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 04:00:00</th>\n",
       "      <td>57</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 06:00:00</th>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 08:00:00</th>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 10:00:00</th>\n",
       "      <td>88</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 12:00:00</th>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 14:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 16:00:00</th>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 18:00:00</th>\n",
       "      <td>94</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00     27       3\n",
       "2050-01-01 02:00:00     67       9\n",
       "2050-01-01 04:00:00     57       8\n",
       "2050-01-01 06:00:00     42       5\n",
       "2050-01-01 08:00:00     35       2\n",
       "2050-01-01 10:00:00     88       7\n",
       "2050-01-01 12:00:00     90       2\n",
       "2050-01-01 14:00:00     59       4\n",
       "2050-01-01 16:00:00     77       1\n",
       "2050-01-01 18:00:00     94       7"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2시간 간격으로 groupby한 후 마지막 값들을 추출\n",
    "df_test.resample('2H').last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.749972Z",
     "start_time": "2022-08-07T14:23:19.737981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>51.5</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 02:00:00</th>\n",
       "      <td>80.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 04:00:00</th>\n",
       "      <td>76.5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 06:00:00</th>\n",
       "      <td>62.5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 08:00:00</th>\n",
       "      <td>45.5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 10:00:00</th>\n",
       "      <td>90.5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 12:00:00</th>\n",
       "      <td>68.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 14:00:00</th>\n",
       "      <td>68.5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 16:00:00</th>\n",
       "      <td>71.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 18:00:00</th>\n",
       "      <td>53.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00   51.5     5.5\n",
       "2050-01-01 02:00:00   80.0     7.0\n",
       "2050-01-01 04:00:00   76.5     4.5\n",
       "2050-01-01 06:00:00   62.5     4.5\n",
       "2050-01-01 08:00:00   45.5     4.5\n",
       "2050-01-01 10:00:00   90.5     6.5\n",
       "2050-01-01 12:00:00   68.0     2.5\n",
       "2050-01-01 14:00:00   68.5     6.5\n",
       "2050-01-01 16:00:00   71.0     3.5\n",
       "2050-01-01 18:00:00   53.0     5.0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2시간 간격으로 groupby한 후 평균 값들을 추출\n",
    "df_test.resample('2H').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.765972Z",
     "start_time": "2022-08-07T14:23:19.753972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>103</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>2050-01-01 02:00:00</th>\n",
       "      <td>160</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 04:00:00</th>\n",
       "      <td>153</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 06:00:00</th>\n",
       "      <td>125</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 08:00:00</th>\n",
       "      <td>91</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 10:00:00</th>\n",
       "      <td>181</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 12:00:00</th>\n",
       "      <td>136</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 14:00:00</th>\n",
       "      <td>137</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 16:00:00</th>\n",
       "      <td>142</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 18:00:00</th>\n",
       "      <td>106</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00    103      11\n",
       "2050-01-01 02:00:00    160      14\n",
       "2050-01-01 04:00:00    153       9\n",
       "2050-01-01 06:00:00    125       9\n",
       "2050-01-01 08:00:00     91       9\n",
       "2050-01-01 10:00:00    181      13\n",
       "2050-01-01 12:00:00    136       5\n",
       "2050-01-01 14:00:00    137      13\n",
       "2050-01-01 16:00:00    142       7\n",
       "2050-01-01 18:00:00    106      10"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2시간 간격으로 groupby한 후 합계 추출\n",
    "df_test.resample('2H').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.780971Z",
     "start_time": "2022-08-07T14:23:19.770971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>103</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>2050-01-01 02:00:00</th>\n",
       "      <td>263</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 04:00:00</th>\n",
       "      <td>416</td>\n",
       "      <td>34</td>\n",
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       "    <tr>\n",
       "      <th>2050-01-01 06:00:00</th>\n",
       "      <td>541</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 08:00:00</th>\n",
       "      <td>632</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 10:00:00</th>\n",
       "      <td>813</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 12:00:00</th>\n",
       "      <td>949</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 14:00:00</th>\n",
       "      <td>1086</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 16:00:00</th>\n",
       "      <td>1228</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 18:00:00</th>\n",
       "      <td>1334</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00    103      11\n",
       "2050-01-01 02:00:00    263      25\n",
       "2050-01-01 04:00:00    416      34\n",
       "2050-01-01 06:00:00    541      43\n",
       "2050-01-01 08:00:00    632      52\n",
       "2050-01-01 10:00:00    813      65\n",
       "2050-01-01 12:00:00    949      70\n",
       "2050-01-01 14:00:00   1086      83\n",
       "2050-01-01 16:00:00   1228      90\n",
       "2050-01-01 18:00:00   1334     100"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2시간 간격으로 groupby한 후 누적 합계 추출\n",
    "df_test.resample('2H').sum().cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.810970Z",
     "start_time": "2022-08-07T14:23:19.783974Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>51.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>103</td>\n",
       "      <td>11</td>\n",
       "      <td>103</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 02:00:00</th>\n",
       "      <td>93</td>\n",
       "      <td>5</td>\n",
       "      <td>67</td>\n",
       "      <td>9</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>160</td>\n",
       "      <td>14</td>\n",
       "      <td>263</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 04:00:00</th>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>8</td>\n",
       "      <td>76.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>153</td>\n",
       "      <td>9</td>\n",
       "      <td>416</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 06:00:00</th>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>62.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>125</td>\n",
       "      <td>9</td>\n",
       "      <td>541</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 08:00:00</th>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>45.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>91</td>\n",
       "      <td>9</td>\n",
       "      <td>632</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 10:00:00</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "      <td>88</td>\n",
       "      <td>7</td>\n",
       "      <td>90.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>181</td>\n",
       "      <td>13</td>\n",
       "      <td>813</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 12:00:00</th>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>136</td>\n",
       "      <td>5</td>\n",
       "      <td>949</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 14:00:00</th>\n",
       "      <td>78</td>\n",
       "      <td>9</td>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>68.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>137</td>\n",
       "      <td>13</td>\n",
       "      <td>1086</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 16:00:00</th>\n",
       "      <td>65</td>\n",
       "      <td>6</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>142</td>\n",
       "      <td>7</td>\n",
       "      <td>1228</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 18:00:00</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>94</td>\n",
       "      <td>7</td>\n",
       "      <td>53.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106</td>\n",
       "      <td>10</td>\n",
       "      <td>1334</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn  price  volumn  price  volumn  price  \\\n",
       "2050-01-01 00:00:00     76       8     27       3   51.5     5.5    103   \n",
       "2050-01-01 02:00:00     93       5     67       9   80.0     7.0    160   \n",
       "2050-01-01 04:00:00     96       1     57       8   76.5     4.5    153   \n",
       "2050-01-01 06:00:00     83       4     42       5   62.5     4.5    125   \n",
       "2050-01-01 08:00:00     56       7     35       2   45.5     4.5     91   \n",
       "2050-01-01 10:00:00     93       6     88       7   90.5     6.5    181   \n",
       "2050-01-01 12:00:00     46       3     90       2   68.0     2.5    136   \n",
       "2050-01-01 14:00:00     78       9     59       4   68.5     6.5    137   \n",
       "2050-01-01 16:00:00     65       6     77       1   71.0     3.5    142   \n",
       "2050-01-01 18:00:00     12       3     94       7   53.0     5.0    106   \n",
       "\n",
       "                     volumn  price  volumn  \n",
       "2050-01-01 00:00:00      11    103      11  \n",
       "2050-01-01 02:00:00      14    263      25  \n",
       "2050-01-01 04:00:00       9    416      34  \n",
       "2050-01-01 06:00:00       9    541      43  \n",
       "2050-01-01 08:00:00       9    632      52  \n",
       "2050-01-01 10:00:00      13    813      65  \n",
       "2050-01-01 12:00:00       5    949      70  \n",
       "2050-01-01 14:00:00      13   1086      83  \n",
       "2050-01-01 16:00:00       7   1228      90  \n",
       "2050-01-01 18:00:00      10   1334     100  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2시간 간격으로 groupby한 후 데이터 특성 추출\n",
    "df_downs = pd.DataFrame()\n",
    "df_downs = pd.concat([df_test.resample('2H').first(),\n",
    "                      df_test.resample('2H').last(),\n",
    "                      df_test.resample('2H').mean(),\n",
    "                      df_test.resample('2H').sum(),\n",
    "                      df_test.resample('2H').sum().cumsum()], axis=1)\n",
    "df_downs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.840969Z",
     "start_time": "2022-08-07T14:23:19.812972Z"
    }
   },
   "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>price</th>\n",
       "      <th>price</th>\n",
       "      <th>price</th>\n",
       "      <th>price</th>\n",
       "      <th>price</th>\n",
       "      <th>price</th>\n",
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       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>27</td>\n",
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       "      <td>51.5</td>\n",
       "      <td>51.5</td>\n",
       "      <td>1200.5</td>\n",
       "      <td>34.648232</td>\n",
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       "    <tr>\n",
       "      <th>2050-01-01 02:00:00</th>\n",
       "      <td>67</td>\n",
       "      <td>93</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>18.384776</td>\n",
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       "    <tr>\n",
       "      <th>2050-01-01 04:00:00</th>\n",
       "      <td>57</td>\n",
       "      <td>96</td>\n",
       "      <td>76.5</td>\n",
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       "      <td>760.5</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 06:00:00</th>\n",
       "      <td>42</td>\n",
       "      <td>83</td>\n",
       "      <td>62.5</td>\n",
       "      <td>62.5</td>\n",
       "      <td>840.5</td>\n",
       "      <td>28.991378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 08:00:00</th>\n",
       "      <td>35</td>\n",
       "      <td>56</td>\n",
       "      <td>45.5</td>\n",
       "      <td>45.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>14.849242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 10:00:00</th>\n",
       "      <td>88</td>\n",
       "      <td>93</td>\n",
       "      <td>90.5</td>\n",
       "      <td>90.5</td>\n",
       "      <td>12.5</td>\n",
       "      <td>3.535534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 12:00:00</th>\n",
       "      <td>46</td>\n",
       "      <td>90</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>968.0</td>\n",
       "      <td>31.112698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 14:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>78</td>\n",
       "      <td>68.5</td>\n",
       "      <td>68.5</td>\n",
       "      <td>180.5</td>\n",
       "      <td>13.435029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 16:00:00</th>\n",
       "      <td>65</td>\n",
       "      <td>77</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>8.485281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 18:00:00</th>\n",
       "      <td>12</td>\n",
       "      <td>94</td>\n",
       "      <td>53.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3362.0</td>\n",
       "      <td>57.982756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  price  price  price   price      price\n",
       "2050-01-01 00:00:00     27     76   51.5   51.5  1200.5  34.648232\n",
       "2050-01-01 02:00:00     67     93   80.0   80.0   338.0  18.384776\n",
       "2050-01-01 04:00:00     57     96   76.5   76.5   760.5  27.577164\n",
       "2050-01-01 06:00:00     42     83   62.5   62.5   840.5  28.991378\n",
       "2050-01-01 08:00:00     35     56   45.5   45.5   220.5  14.849242\n",
       "2050-01-01 10:00:00     88     93   90.5   90.5    12.5   3.535534\n",
       "2050-01-01 12:00:00     46     90   68.0   68.0   968.0  31.112698\n",
       "2050-01-01 14:00:00     59     78   68.5   68.5   180.5  13.435029\n",
       "2050-01-01 16:00:00     65     77   71.0   71.0    72.0   8.485281\n",
       "2050-01-01 18:00:00     12     94   53.0   53.0  3362.0  57.982756"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2시간 간격으로 price의 기초통계량 추출\n",
    "df_downs = pd.DataFrame()\n",
    "df_downs = pd.concat([df_test.price.resample('2H').min(),\n",
    "                      df_test.price.resample('2H').max(),\n",
    "                      df_test.price.resample('2H').mean(),\n",
    "                      df_test.price.resample('2H').median(),\n",
    "                      df_test.price.resample('2H').var(),\n",
    "                      np.sqrt(df_test.price.resample('2H').var())], axis=1)\n",
    "df_downs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.885973Z",
     "start_time": "2022-08-07T14:23:19.842972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price_min</th>\n",
       "      <th>price_max</th>\n",
       "      <th>price_mean</th>\n",
       "      <th>price_median</th>\n",
       "      <th>price_var</th>\n",
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       "      <th>2050-01-01 00:00:00</th>\n",
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       "      <td>65.75</td>\n",
       "      <td>71.5</td>\n",
       "      <td>783.583333</td>\n",
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       "    <tr>\n",
       "      <th>2050-01-01 04:00:00</th>\n",
       "      <td>42</td>\n",
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       "      <td>70.0</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>24.474477</td>\n",
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       "    <tr>\n",
       "      <th>2050-01-01 08:00:00</th>\n",
       "      <td>35</td>\n",
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       "    <tr>\n",
       "      <th>2050-01-01 12:00:00</th>\n",
       "      <td>46</td>\n",
       "      <td>90</td>\n",
       "      <td>68.25</td>\n",
       "      <td>68.5</td>\n",
       "      <td>382.916667</td>\n",
       "      <td>19.568257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 16:00:00</th>\n",
       "      <td>12</td>\n",
       "      <td>94</td>\n",
       "      <td>62.00</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1252.666667</td>\n",
       "      <td>35.393031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price_min  price_max  price_mean  price_median  \\\n",
       "2050-01-01 00:00:00         27         93       65.75          71.5   \n",
       "2050-01-01 04:00:00         42         96       69.50          70.0   \n",
       "2050-01-01 08:00:00         35         93       68.00          72.0   \n",
       "2050-01-01 12:00:00         46         90       68.25          68.5   \n",
       "2050-01-01 16:00:00         12         94       62.00          71.0   \n",
       "\n",
       "                       price_var  price_std  \n",
       "2050-01-01 00:00:00   783.583333  27.992559  \n",
       "2050-01-01 04:00:00   599.000000  24.474477  \n",
       "2050-01-01 08:00:00   752.666667  27.434771  \n",
       "2050-01-01 12:00:00   382.916667  19.568257  \n",
       "2050-01-01 16:00:00  1252.666667  35.393031  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4시간 간격으로 price의 기초통계량 추출\n",
    "# 각각 변수를 선언하여 컬럼명을 바꿀수도 있음\n",
    "df_downs = pd.DataFrame()\n",
    "df_downs['price_min'] = df_test.price.resample('4H').min()\n",
    "df_downs['price_max'] = df_test.price.resample('4H').max()\n",
    "df_downs['price_mean'] = df_test.price.resample('4H').mean()\n",
    "df_downs['price_median'] = df_test.price.resample('4H').median()\n",
    "df_downs['price_var'] = df_test.price.resample('4H').var()\n",
    "df_downs['price_std'] = np.sqrt(df_test.price.resample('4H').var())\n",
    "df_downs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.915971Z",
     "start_time": "2022-08-07T14:23:19.888974Z"
    }
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>69.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 06:00:00</th>\n",
       "      <td>66.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 12:00:00</th>\n",
       "      <td>69.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 18:00:00</th>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Price\n",
       "2050-01-01 00:00:00  69.333333\n",
       "2050-01-01 06:00:00  66.166667\n",
       "2050-01-01 12:00:00  69.166667\n",
       "2050-01-01 18:00:00  53.000000"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 함수화를 통해 다양한 빈도에서의 Downsample 집계 가능\n",
    "def resample_ts(df, freq, col_num):\n",
    "    df_downs = pd.DataFrame()\n",
    "    df_downs['price_min'] = df.price.resample(freq).min()\n",
    "    df_downs['price_max'] = df.price.resample(freq).max()\n",
    "    df_downs['price_mean'] = df.price.resample(freq).mean()\n",
    "    df_downs['price_median'] = df.price.resample(freq).median()\n",
    "    df_downs['price_var'] = df.price.resample(freq).var()\n",
    "    df_downs['price_std'] = np.sqrt(df.price.resample(freq).var())\n",
    "    result = df_downs.iloc[:,[col_num]]\n",
    "    result.columns = ['Price']\n",
    "    return result\n",
    "\n",
    "resample_ts(df_test, '6H', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:19.931972Z",
     "start_time": "2022-08-07T14:23:19.917975Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:30:00</th>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:00:00</th>\n",
       "      <td>93</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:30:00</th>\n",
       "      <td>67</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 02:00:00</th>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-03-30 22:00:00</th>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-03-30 22:30:00</th>\n",
       "      <td>47</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-03-30 23:00:00</th>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-03-30 23:30:00</th>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-03-31 00:00:00</th>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4273 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00     76       5\n",
       "2050-01-01 00:30:00     27       5\n",
       "2050-01-01 01:00:00     93       2\n",
       "2050-01-01 01:30:00     67       5\n",
       "2050-01-01 02:00:00     96       4\n",
       "...                    ...     ...\n",
       "2050-03-30 22:00:00     28       5\n",
       "2050-03-30 22:30:00     47       7\n",
       "2050-03-30 23:00:00     74       1\n",
       "2050-03-30 23:30:00     31       9\n",
       "2050-03-31 00:00:00     90       2\n",
       "\n",
       "[4273 rows x 2 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임의 시계열 인덱스 생성\n",
    "# 가격과 거래량 데이터 생성\n",
    "df_test = pd.DataFrame(index=pd.date_range(start='2050-01-01', end='2050-03-31', \n",
    "                                           freq='30min'))\n",
    "np.random.seed(123)\n",
    "if 'price' not in df.columns:\n",
    "    df_test['price'] = np.random.randint(low=10, high=100, size=df_test.shape[0])\n",
    "    df_test['volumn'] = np.random.randint(low=1, high=10, size=df_test.shape[0])\n",
    "df_test            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.022971Z",
     "start_time": "2022-08-07T14:23:19.935977Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-31</th>\n",
       "      <td>696.284832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-02-28</th>\n",
       "      <td>686.208940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-03-31</th>\n",
       "      <td>658.029654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Price\n",
       "2050-01-31  696.284832\n",
       "2050-02-28  686.208940\n",
       "2050-03-31  658.029654"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 다양한 빈도에서의 Downsample 집계 가능\n",
    "# 1D, 1W, 2W, 1M\n",
    "def resample_ts(df, freq, col_num):\n",
    "    df_downs = pd.DataFrame()\n",
    "    df_downs['price_min'] = df.price.resample(freq).min()\n",
    "    df_downs['price_max'] = df.price.resample(freq).max()\n",
    "    df_downs['price_mean'] = df.price.resample(freq).mean()\n",
    "    df_downs['price_median'] = df.price.resample(freq).median()\n",
    "    df_downs['price_var'] = df.price.resample(freq).var()\n",
    "    df_downs['price_std'] = np.sqrt(df.price.resample(freq).var())\n",
    "    result = df_downs.iloc[:,[col_num]]\n",
    "    result.columns = ['Price']\n",
    "    return result\n",
    "\n",
    "resample_ts(df_test, '1D', 4)\n",
    "resample_ts(df_test, '1W', 4)\n",
    "resample_ts(df_test, '2W', 4)\n",
    "resample_ts(df_test, '1M', 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upsampling에서 데이터 집계\n",
    "\n",
    "- `resample` 함수는 `asfreq` 보다 더욱 많은 기능이 포함되어 있으며, `데이터 증가에 따른 결측값 처리`을 편리하게 지원"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.052973Z",
     "start_time": "2022-08-07T14:23:20.024972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:00:00</th>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 02:00:00</th>\n",
       "      <td>93</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 03:00:00</th>\n",
       "      <td>67</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 04:00:00</th>\n",
       "      <td>96</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 05:00:00</th>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 06:00:00</th>\n",
       "      <td>83</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 07:00:00</th>\n",
       "      <td>42</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 08:00:00</th>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 09:00:00</th>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 10:00:00</th>\n",
       "      <td>93</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 11:00:00</th>\n",
       "      <td>88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 12:00:00</th>\n",
       "      <td>46</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 13:00:00</th>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 14:00:00</th>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 15:00:00</th>\n",
       "      <td>59</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 16:00:00</th>\n",
       "      <td>65</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 17:00:00</th>\n",
       "      <td>77</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 18:00:00</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 19:00:00</th>\n",
       "      <td>94</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 20:00:00</th>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 21:00:00</th>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 22:00:00</th>\n",
       "      <td>94</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:00:00</th>\n",
       "      <td>57</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-02 00:00:00</th>\n",
       "      <td>71</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00     76       1\n",
       "2050-01-01 01:00:00     27       8\n",
       "2050-01-01 02:00:00     93       4\n",
       "2050-01-01 03:00:00     67       5\n",
       "2050-01-01 04:00:00     96       7\n",
       "2050-01-01 05:00:00     57       2\n",
       "2050-01-01 06:00:00     83       6\n",
       "2050-01-01 07:00:00     42       7\n",
       "2050-01-01 08:00:00     56       3\n",
       "2050-01-01 09:00:00     35       2\n",
       "2050-01-01 10:00:00     93       9\n",
       "2050-01-01 11:00:00     88       4\n",
       "2050-01-01 12:00:00     46       6\n",
       "2050-01-01 13:00:00     90       1\n",
       "2050-01-01 14:00:00     78       3\n",
       "2050-01-01 15:00:00     59       7\n",
       "2050-01-01 16:00:00     65       3\n",
       "2050-01-01 17:00:00     77       5\n",
       "2050-01-01 18:00:00     12       5\n",
       "2050-01-01 19:00:00     94       7\n",
       "2050-01-01 20:00:00     49       4\n",
       "2050-01-01 21:00:00     76       1\n",
       "2050-01-01 22:00:00     94       7\n",
       "2050-01-01 23:00:00     57       5\n",
       "2050-01-02 00:00:00     71       8"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임의 시계열 인덱스 생성\n",
    "# 가격과 거래량 데이터 생성\n",
    "df_test = pd.DataFrame(index=pd.date_range(start='2050-01-01', end='2050-01-02', \n",
    "                                           freq='1H'))\n",
    "np.random.seed(123)\n",
    "if 'price' not in df.columns:\n",
    "    df_test['price'] = np.random.randint(low=10, high=100, size=df_test.shape[0])\n",
    "    df_test['volumn'] = np.random.randint(low=1, high=10, size=df_test.shape[0])\n",
    "df_test            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.067974Z",
     "start_time": "2022-08-07T14:23:20.054974Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>76.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:20:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:40:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:00:00</th>\n",
       "      <td>27.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:20:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 22:40:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:00:00</th>\n",
       "      <td>57.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:20:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:40:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-02 00:00:00</th>\n",
       "      <td>71.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00   76.0     1.0\n",
       "2050-01-01 00:20:00    NaN     NaN\n",
       "2050-01-01 00:40:00    NaN     NaN\n",
       "2050-01-01 01:00:00   27.0     8.0\n",
       "2050-01-01 01:20:00    NaN     NaN\n",
       "...                    ...     ...\n",
       "2050-01-01 22:40:00    NaN     NaN\n",
       "2050-01-01 23:00:00   57.0     5.0\n",
       "2050-01-01 23:20:00    NaN     NaN\n",
       "2050-01-01 23:40:00    NaN     NaN\n",
       "2050-01-02 00:00:00   71.0     8.0\n",
       "\n",
       "[73 rows x 2 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1시간 단위의 데이터를 20분 단위의 데이터로 변환\n",
    "# 비어있는 Timestamp 때문에 결측값이 생성\n",
    "df_test.resample('20min').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling에서의 결측치처리\n",
    "\n",
    "> **\"결측값이 있을 때 통상 `fillna` 또는 `interpolate` 함수를 사용하여 채움\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.083976Z",
     "start_time": "2022-08-07T14:23:20.070973Z"
    }
   },
   "outputs": [
    {
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       "      <td>57.0</td>\n",
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       "      <td>57.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>2050-01-02 00:00:00</th>\n",
       "      <td>71.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00   76.0     1.0\n",
       "2050-01-01 00:20:00   76.0     1.0\n",
       "2050-01-01 00:40:00   76.0     1.0\n",
       "2050-01-01 01:00:00   27.0     8.0\n",
       "2050-01-01 01:20:00   27.0     8.0\n",
       "...                    ...     ...\n",
       "2050-01-01 22:40:00   94.0     7.0\n",
       "2050-01-01 23:00:00   57.0     5.0\n",
       "2050-01-01 23:20:00   57.0     5.0\n",
       "2050-01-01 23:40:00   57.0     5.0\n",
       "2050-01-02 00:00:00   71.0     8.0\n",
       "\n",
       "[73 rows x 2 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ffill 설정을 통해 위 샘플로 아래 결측치를 채움\n",
    "df_ups = df_test.resample('20min').mean()\n",
    "df_ups.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.099970Z",
     "start_time": "2022-08-07T14:23:20.086973Z"
    }
   },
   "outputs": [
    {
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       "      <td>71.0</td>\n",
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       "      <th>2050-01-02 00:00:00</th>\n",
       "      <td>71.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00   76.0     1.0\n",
       "2050-01-01 00:20:00   27.0     8.0\n",
       "2050-01-01 00:40:00   27.0     8.0\n",
       "2050-01-01 01:00:00   27.0     8.0\n",
       "2050-01-01 01:20:00   93.0     4.0\n",
       "...                    ...     ...\n",
       "2050-01-01 22:40:00   57.0     5.0\n",
       "2050-01-01 23:00:00   57.0     5.0\n",
       "2050-01-01 23:20:00   71.0     8.0\n",
       "2050-01-01 23:40:00   71.0     8.0\n",
       "2050-01-02 00:00:00   71.0     8.0\n",
       "\n",
       "[73 rows x 2 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bfill 설정을 통해 아래 샘플로 위 결측치를 채움\n",
    "df_ups.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.114971Z",
     "start_time": "2022-08-07T14:23:20.103972Z"
    }
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   "outputs": [
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       "      <td>71.0</td>\n",
       "      <td>8.0</td>\n",
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       "<p>73 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00   76.0     1.0\n",
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       "2050-01-01 00:40:00  100.0   100.0\n",
       "2050-01-01 01:00:00   27.0     8.0\n",
       "2050-01-01 01:20:00  100.0   100.0\n",
       "...                    ...     ...\n",
       "2050-01-01 22:40:00  100.0   100.0\n",
       "2050-01-01 23:00:00   57.0     5.0\n",
       "2050-01-01 23:20:00  100.0   100.0\n",
       "2050-01-01 23:40:00  100.0   100.0\n",
       "2050-01-02 00:00:00   71.0     8.0\n",
       "\n",
       "[73 rows x 2 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value 설정을 통해 특정 값으로 결측치를 채움\n",
    "df_ups.fillna(value=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.144972Z",
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      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00  76.00     1.0\n",
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       "2050-01-01 01:00:00  27.00     8.0\n",
       "2050-01-01 01:20:00  67.24     4.8\n",
       "...                    ...     ...\n",
       "2050-01-01 22:40:00  67.24     4.8\n",
       "2050-01-01 23:00:00  57.00     5.0\n",
       "2050-01-01 23:20:00  67.24     4.8\n",
       "2050-01-01 23:40:00  67.24     4.8\n",
       "2050-01-02 00:00:00  71.00     8.0\n",
       "\n",
       "[73 rows x 2 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value 설정을 통해 특정 값으로 결측치를 채움\n",
    "df_ups.fillna(value=df_ups.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.174970Z",
     "start_time": "2022-08-07T14:23:20.146971Z"
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       "    <tr>\n",
       "      <th>2050-01-01 00:40:00</th>\n",
       "      <td>67.24</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:00:00</th>\n",
       "      <td>27.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:20:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 22:40:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:00:00</th>\n",
       "      <td>57.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:20:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:40:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-02 00:00:00</th>\n",
       "      <td>71.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  volumn\n",
       "2050-01-01 00:00:00  76.00     1.0\n",
       "2050-01-01 00:20:00  67.24     4.8\n",
       "2050-01-01 00:40:00  67.24     4.8\n",
       "2050-01-01 01:00:00  27.00     8.0\n",
       "2050-01-01 01:20:00    NaN     NaN\n",
       "...                    ...     ...\n",
       "2050-01-01 22:40:00    NaN     NaN\n",
       "2050-01-01 23:00:00  57.00     5.0\n",
       "2050-01-01 23:20:00    NaN     NaN\n",
       "2050-01-01 23:40:00    NaN     NaN\n",
       "2050-01-02 00:00:00  71.00     8.0\n",
       "\n",
       "[73 rows x 2 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# limit 설정을 통해 특정 값으로 결측치를 채우는 갯수를 제한\n",
    "df_ups.fillna(value=df_ups.mean(), limit=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.249971Z",
     "start_time": "2022-08-07T14:23:20.227970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:20:00</th>\n",
       "      <td>59.666667</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:40:00</th>\n",
       "      <td>43.333333</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:00:00</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:20:00</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 22:40:00</th>\n",
       "      <td>69.333333</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:00:00</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:20:00</th>\n",
       "      <td>61.666667</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:40:00</th>\n",
       "      <td>66.333333</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-02 00:00:00</th>\n",
       "      <td>71.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         price    volumn\n",
       "2050-01-01 00:00:00  76.000000  1.000000\n",
       "2050-01-01 00:20:00  59.666667  3.333333\n",
       "2050-01-01 00:40:00  43.333333  5.666667\n",
       "2050-01-01 01:00:00  27.000000  8.000000\n",
       "2050-01-01 01:20:00  49.000000  6.666667\n",
       "...                        ...       ...\n",
       "2050-01-01 22:40:00  69.333333  5.666667\n",
       "2050-01-01 23:00:00  57.000000  5.000000\n",
       "2050-01-01 23:20:00  61.666667  6.000000\n",
       "2050-01-01 23:40:00  66.333333  7.000000\n",
       "2050-01-02 00:00:00  71.000000  8.000000\n",
       "\n",
       "[73 rows x 2 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# interpolate 함수를 통해 값들을 선으로 이어 생성된 값으로 채움\n",
    "df_ups.interpolate(method='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.429971Z",
     "start_time": "2022-08-07T14:23:20.252977Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# interpolate 함수를 통해 값들을 선으로 이어 생성된 값으로 채움\n",
    "# 시각화로 채워진 값을 유추 가능\n",
    "df_ups.interpolate(method='linear').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.612969Z",
     "start_time": "2022-08-07T14:23:20.432972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kx1s6PQC4JjRJEryMweWhiE2809LlxoEaXo4+0ET23hiZlwaTBDR2uFHb5kReepLSSySIqDnZ1AkfYxiSFXrE3eh0ub1Yu4/bCS6ZWIjJxZm4sKwAP39rBz47UIfffrgP//7mJJ68YhImDcnUd7EGh6ShwfjWt76F7Oxs7Nu3D9dcc418/+WXX44//OEP+O1vf4sJEybgueeew8qVKzF37txeX8dqteLVV1/F3r17MXnyZDzxxBP49a9/DQBwe31od/mFDyAPnBTpLiJ+2XaMNy4syU5Bblr4xsgkqxnDcnj0dD91cCYMiMfrw2V/+i8u+sNnqG116r0cxfjsQB06XF4UOpIwaYgDAFCcnYK//uB0/P7KychKsWJvVSuuf/ErdPiP70TvSCwG8xstLS1wOBxobm5GRkZGt8e6urpQXl6O0tJSJCXR1Whv1Lc7caKxEyk2M0bmpeNEUyfq25zITbOjKDNZlfekv4sxWP7Rfiz/6AAun1KE5VdNjeg17nh5Mz7YVYVffHs8bj67VOEVak+X2wur2QSzidJ28UBNSxdOX/IxAODuc0fi/vljdF6RMvzk79/gjS3HcdOsYVi8YMIpjze0uzB/+aeobXXitdvOwBnDc3RY5cD0d/7WCor4JCAizZWRxA3KyRTxSRi2HGsCEFmaSzA6jkZXnGjqxNlPrMV3/7yBPG5xQl2bS/7/yxuPot0Z+9EPt9eHj/dWAwAuLCvodZvsVBtOG8a/11v933Oid0j4qITT7cWeyhbUtHbpvZRueH0Mbf4DQUZyd+FDBuf4xudj2BZFRZdgrBA+MT66gjGGR/+1E3VtTmw51oRtFU16L4lQgPr2QHqrudON1zdV6LgaZfiqvAFNHW7kpNpw2rC++2dNKc4EAGyraNRoZbEJCR+VaOnywO31oabFCY/XOKbh1i43GGOwW8ywW/ifP8lqIoNzAtDQ4UJLFxe9o/PDa1wYjHjugepW+HyxK5Q/3FWFj/YEek/9a5tOLS8IRWlo5xEfkbp88fNyuA10DI6ED/zVXBeMz+83JTu1JBDxoYvYviHhoxIuD08b+RhDfbtrgK21Q05zJVvkUmRJksjgnADUtPAr4dw0G2yWyL/6w3JSYLOY0OHy4nhjp1LL05TWLjcefWcXAMjpgXe3nzTURQoRGSLV9a2xechJteFEUyfe31Gp86oix+dj+HAXFz7z+0hzCcqKHDCbJNS0OlHZbKxsg5Eg4aMSzqDISX2byxBXxj7G5OGSwt8jSLZx4SM6OBPxR7U/7RptCbrFbMLIQbyz974Y7eD81Or9qG5xYmhOClZ+/3Rkp9pQ1+bCfw/V6700Ikrq27jAH5yZjJtmDQMAPLf+cMxGQLZWNKGm1Yl0uwWzRvRvWE62mTHOP4aGUrd9E7fCR+8uwiJlJEGCx+dDU6f+06zbnR54GYPFbEKKX+gI1DY46/33IIBaf8QnLyP6+T6xPLrim4omvPTFEQDAry8vQ5rdgm9PKgQA/GvrCR1XRihBvT/ik5Nqw3VnDEWy1YzdlS3478HYFLUi2vOtcXmwW8wDbB3w+YhBxMSpxF0DQ5vNBpPJhJMnT2LQoEGw2Wyad5f1+RicTn51nZliQ2OHC9WNXiSbUnTtdNvQ3AnmcSPFapXngQlMXi+Yx4UOrxudnWbF1skYg8vlQm1tLUwmE2w2myKvS4RPdQv/TOYr0HRwtDy6IraEj8frw0Nv7gBjwOVTijB7FO9wftmUwfjrF0fx4a4qdLq8cgSUiD2EtSAnzY6sVBuuPK0Yf9lwBM99eghnj8rVeXXhwRiT/T0XTug/zSWYWpyFv208RhGffog74WMymVBaWorKykqcPKmPWVGYmk0SYHEkoa65Cz4GdDXYZC+N1jAGVLV0wetj8KXZ0Nlg7vE4Q21zFxgD0GqHReG25ykpKSgpKZFniBHaI6e6FIj4iMqu/TEmfP6y4Qh2V7bAkWzFI98eL98/rSQTJdkpONbQgTV7qrFgcpGOqySiQVR1Zafyi6ybzy7FyxuP4rMDddh1shkTihx6Li8s9lS24lhDB+wWE+aMGRTSc6b4R9FsP97M5zHSCItTiDvhA/CoT0lJCTweD7xe7T0r/z1Yh8Vrd2JUfhr+fN14/GftAby15QSmD83Ck1dM1nw9ALDrZDN+8dZWpNos+OcPZ/Vqbn3qlS3YW9WCn188DueNzFfsvc1mMywWC8110pkaOdWlQMTHL3wO1bbB5fFFZZbWiuONHXhq9X4AwM8vHtutc7UkSbhsShGe/uQg/rX1BAmfGEakunLTuPApzk7BJRML8c43J/H8p4fxhwgbd+rBB/4015zRg5BiC+10XZqTCkeyFc2dbuyrakXZ4NgReloRl8IHCEwZ7zlFXAsON7hwotWLqaUpSEpKwpUzR+CZT4/hnZ11+OF5Lowr1L5b5Zp9R3Ci1YtLJ+cjI633+TWDczPw8YFGbDvZjkumUnfleKPa374/Pz36iE+RIwnpdgtanR6U17XLnh+jwhjD//vXLnS6vTh9WDa+O734lG0umzIYT39yEOv316Kh3SVHDIjYoiEo1SW47ZzheOebk3h3eyV+On9MzMzwEkNJ+2pa2Bsmk4TJxZn4dH8tth5rJOHTC8a/TItByuvbAUCeaVScnYKLyrh58sXPy3VZ0+rd/As0b3zfkRzxBdlxolmTNRHaUtMiUl3Ri1pJkgIdnGPA4PzBzip8srcGVrOE33ynDKZeeqGMzEtD2eAMeHwM78Vw+XMi0+X2yg1ag4Vr2WAHzh6ZC6+P4f8+P6LT6sLjcG0b9lW3wmKScN7Y8CLwU4XBmXw+vULCRwWOCuGTmyrfd8tsPtPoX9tOyCcgrThY04bDte2wmU2Y20+eeKJf+Ow80WKI8ntCOXw+Jg9szFfA4wMEDM5G9/l4fQyPvbsbAHDHnBEY1U/zxsunDAZA1V2xijA2W80SMpK6JzRuO2c4AOC1TcfQ3KF/le1AfLiLj6g4c0QOHCnhZS6Ez2cbja7oFRI+KnCkrgMAUJobCKdOLcnCjKFZcHuZXEqrFaIcctbIHKQn9f0FGpWXBrvFhDanB0f84o2IDxo6XPD4GCQJEU1l740x+byXj9EruyqbO1HZ3AWrWcJd547sd9tLJxdBkoCvjzaioqFDoxUSStEgl7LbT/EUzh6Vi3GFGehwefG3L4/qsbywEP6ecNJcgilDMgEAh+va0dRhnAa6RoGEj8J0ub042cy72YpUl+CW2fyK428bj6HDpd3gvNW7+ZXDvPH9f4EsZpPsP6J0V3whStlzUm2KVXmMKeCfFaP38jlWzwXMkKyUAasq8zOS5CZx73xDIyxijboeFV3BSJKE2/1Rn1c2Glv4nGzqxDcVTZAkPqYiXLJSbSj1ZxyorP1USPgozLGGDjAGpNstp3z5Lhifj6E5KWjudOONzcc1WY/H68Muv4iZHUIPi0C6i4RPPFHjT3NF27U5mNH+iM+xhg5NhXy4HPVHbkqyQzO0XjaZp7ve3noiZrv9Jipy88K03o3pc0bzVP/J5i44PcbtUr/aH+2ZMTQr4u9sYGBpk0Krih9I+CjMkbqAv6dnqNVskvCDs7jX58XPy+HVwEdzoqkTHh+D3WLC4MzkAbefOIQMzvGI8JUp5e8BeNWMSJvtN/Ck9qP+iM/QnNCEz4UTC2CzmHCgpg27K1vUXBqhMGJcRV/p3MwUK2z+iKfwvBmRNXt4lH5+iE0Le2Oq3+ezlXw+p0DCR2GO9GJsDua7M4bAkWzFkfoOfOT/cKtJuV+IDc1J6bWSpSci4rOLDM5xhdzDR8GIDxAbjQwrwoz4ZCRZcd7YPAA0sT3WkEvZ+2hFIEkSBvnbOdQYVPgwxrDzBBfcZw4wm6s/giM+FLnsDgkfhSkXxuY+ri5TbBZcdRrvISJakau7Hi58SvsQYj0RBudWMjjHFaJrs5IRHyBQ2WXkkvajDUL8h/YdAHhPHwB4Z9tJTSKzhDKIyezZfaS6AASET4sxhU9dmwvNnW6YJGCEfxhwJIwtyIDdYkJzp1s+DxAcEj4Kc6Ru4IPs1JIsALzrrVbr6SsC1RMyOMcn1Qp2bQ5G+HwO1Bgz1cUYCzvVBQDnjh2EjCQLqlq68GV5bA63TETEuIrc1L4FvhA+tW3GFD4HavhFRHH2wGb8/rBZTHJvNvL5dIeEj8L01sOnJyPz+GOHa9tVD0GW14sIVOhXu2Rwjj8C5mZlIz7F/vTRyaZORV9XKZo63Gjt4sbrUFNdAGC3mHHxRDGxndJdsUKga3PfER/xHajVuJ9aqBz0X0SMyos82iOQGxmSz6cbJHwUhJey8y9Tf6mlkuxUmE0S2pwe1fPM4UZ8gIDwoYhP/BAwNysb8Slw8NerbOo0pI9AVHTlZ9jDvnq+1D+va+2+GkPuG3Eqoqqrv3EjwudmVI+PED4j86IfAyM3MqSITzdI+CiICKlnJFmQ1U+nTZvFhOIsXmGlZrrL5fHheKNophi68Ckjg3Nc0b1rs7LCp8jBP8ftLi9ancYraRcR2HCiPYJpJVmwmCTUtDrlCxrCuDDGUDdAVRcA5GUY29x8oFoIHwUiPn5bxZ7KFnS5jVu+rzUkfBSkvJ9S9p4I09rhWvVMZxWNHfAxIMVmDivFMSo/YHA+St1rY57uXZuVHbyZbDMj0y/yK5uMJw5E88KS7NCFvyDZZpb9bluONiq6LkJ5OlxeOD0+AP2nugb5RZFRy9kPKJjqKnIkYVC6HR4fI+tCECR8FORoj+Gk/TF8EN9GzYiPnObKGViIBWMlg3NcEejabIdFoa7NwRT6oz6iY7mREMI9HGNzMNOoF0rMINJcSVYTUmyWPrcLRHyMJ9SbOlxy1GqEAsJHkiTy+fQCCR8FGaiHTzDDNYj4hFvKHozs8znepOSSCB0I9PBR1tgsKJJ9PsY7kRyLoKIrGJEq2HKMIj5GR4yryOmnogsIeHzq2lyGa1Ug/D1FjiSk2fsWb+FAPp9TIeGjIAGhMfBBVk511akX8Qmk3sI/6JPBOX6oUamHj0A2OBsy4hO5xwfgPh8A2H2yxdAjDojAgNKB0rk5aTZIEuD1MbkKzCiINNfI/OiNzYKpxfwzvJXEuwwJHwURU9lDaZQmUl3HGztVM50dCSP11hNhcN59klr2xzqih4/SxmZBkX8USqXBDMBdbq+87+E0LwymODsZOak2uLw+uZsuYUxED5+cfozNAE/lZ6dwcWS0dJeSpeyCSUMcMEl8Plm1QUv4tYaEj0J0uryo8n+oQumZk5NqQ0aSBYxBtQ7JQohFkuoSqYGWLg9autyKrovQFnFwVyvVVWjQiI8YVZFu77/Ksj8kSZLTXXTFbGzqQihlF8hNDA1mcFbS2CxItVvkDuvk8+GQ8FEIEVJ3JFuRFcIXT5Ik2bymhs+H9xTiJ6JwevgIUu0WQ1frEKGjVtdmgTA3G+1zItpLlOSkhGXu7wkNe4wNBprMHoz4LhitpP2gf/SLEqXswUwln083SPgoRCSNAofn8g/3IRXa/R9r6ABj/Gq3r4F9AyF6tBi1Ky8RGmo1LxSIiM/JZmM1MYy2okswjSI+MUFDCOMqBHkGjPi0OT1yvyilhc8UubKLPsMACR/FEMNJh4VxkBU+n8MqDJCTjdaDwitlD0Z4N06Q8Ilp1BpXIRDm5i63D82dxkmLHpObF0bm7xEEeySqDOZjIgLUt4ee6sqTB5Ua5+8pLoAHpduRmaJsv60pfoPzzhPNhro40QsSPgoRTg8fgajsUqOXT3ld5MZmweBM/5U8CZ+YxedjsvBRK+KTZDXLUcWTBkp3iYhPpBVdglS7BWMLeF+rRLli9vlYzJ0g68JIdRlxUKlc0RXFRPa+KM3lY5LaXV7Dpff0gISPQkTSM2fEIPWGlUaSeuuJiPiQ8Ild6tt5rxI1ujYHU5hpPINztD18ghEeiUTo53OiqRNTHluNn/z9G72XEhZyqmuAqi4gaF5Xi3FEgJjKPipfeeETPCZJzd5xsQIJH4UIp3mhoCQnBSaJ53aVzjWH01OoLwLCxzhX8UR4iIoutbo2CwoyRPdmY3xWvD6GikZlIj5AsM+nKerXMjrvb69ES5cHb287gUaD9bnpC8ZYSANKBUac13VIhYquYMRFebkK1opYg4SPAnS4PHLlTDgeH7vFLB+UDyqc7oqmh4/AKB6fv35xBLOWfoyD/isiInRq5B4+6vh7BEX+iE+VQSI+lc2dcHsZrGZJ/hxHg4j4bD/RDJd/HlS8sn5/LQDAx4CP9lTrvJrQaOn0wOPvwhyWx6e1yzApPZHqUmJURW+U+otpylVsmhsrkPBRAFE2m5liDduUpsboimAhFkkPH8Fg/wmjqqVLt9buPh/Dn9YexMnmLryz7aQua4hlqlWu6BIYraRdpLmGZKXAbIq8lF1QmpuKzBQrXB4f9lTGbyPDDpcHX5U3yLdX744N4SOaF6bbLUiymgfcXnh8utw+tDk9qq4tFLrcXhzze9JG5SnXtTmY0iBrRaJDwkcBjkRhJB6eq/yHUTQuzIpAiAUzKN0Oi0mC18d063C67XiTLOK+OU7jM8JF7YougYj4GGVQqVLGZkHwsMd49vl8ebgBLq8PKTYuHj7dX4sOl/7CYCBERVcoxmYASLFZ5FlYRkh3cZ8nv3hWy4s3nFJdMiR8FKC8PvJhoCKsqWRlVyR+o94wmyS5VFkvg/OHO6vk/28/3mSYsHSsICI+ajUvFBRkCHOzMSI+RxU0NgsSwecj0lyXTx2MIVnJcHp8+HR/nc6rGph6f3VWKGkuQaCkXX/hI4zNIwelRdVssz9E+5RjDR1we+M7XTsQJHwUQER8IjnIyhEfBfOusrE5Cn+PIODz0f6ExhjDB7sCwqexw43jjcaIKMQKgVJ2tSM+gXldRhCnFQpHfIDEmNQuhM+c0YMwf0IBAGB10HfQqARK2UP/nA8K8vnojTyjS4WKLkF+ehKSrWZ4fCzhj6MkfBTgSH3kM7GEx0fJYaXlCpSyCwbrWNK+t6oVR+s7YLOY5E6m3xxv0nwdsYxo0CbKd9UiPyMJkgS4PD5DTLwWI2QiHU7aG5OLHZAk/l01wslSaY7Vd6C8rh0Wk4RZI3Iwb3w+AODjvTWGjxCIz1w4aSIjzesSwmekSv4eADCZJPmckOgGZxI+ChCNxyc3TflhpUr08BEU6djE8AN/muucUYNwxvBsAMB28vmERbVGVV02i0nun6J3uosxpkqqKz3JitH+E9O2OEx3rT/Aoz3ThmYhPcmKGcOykZ1qQ3Onu5vh2YhElurixzYjCB+5eaFKFV0CNTylsYjiwsfj8eCRRx5BaWkpkpOTMXz4cDz22GPw+QJXDIwxLF68GEVFRUhOTsbcuXOxa9cupZeiCe1Oj5xOiET4SJKkeGWXEFBKprr0ED4f+kPsF5YVYNKQTADANzRkL2R8PiZ3plW7qgsImtmlc/uDpg43Wru4Ibc4SznhAwDThmYCALbEo/DZF0hzAdzjd/64PADGT3fVCXNzCHO6BEbp5ePy+OSLVbV6+Aiolw9HceHzxBNP4M9//jNWrFiBPXv24Mknn8Rvf/tbPP300/I2Tz75JJYtW4YVK1Zg06ZNKCgowAUXXIDW1tjr0yJERlaKFQ7/NPNwkWd2KWBwbu1yy/nuYVE0LxTo5fEpr2vH3qpW+eA72S98dp5o1q20PtYI7toc6aDacBDCR++Ij6joyku3I9k2cGlzOEwtjs+BpS6PDxsOcROzED4AAj6f3dWG8G71RUMY4yoEeQbx+Bytb4fHx5BqM8vfIbUg4cNRXPh88cUXuOyyy3DJJZdg2LBhuOKKKzBv3jx8/fXXAHi0Z/ny5Xj44YexcOFClJWV4aWXXkJHRwdWrVql9HJUR4TUo0krBWZ2Rf9hFKXsuWl2pCdFJsSCGSybVrW9ihfRnjOH5yAzxYaReWlIsZnR7vIqIhATAVHRlZumbtdmgdzLR2/hUx95scFAiIjP9uPN8Bjc9xIOXx9tQIfLi9w0O8YXZsj3nzUyFyk2Myqbu7DjhHHTzKKPT1gRH4OMrZD9PfnpqlV0CaiXD0fxo+HZZ5+Njz/+GPv37wcAfPPNN/j8889x8cUXAwDKy8tRVVWFefPmyc+x2+2YM2cONmzY0OtrOp1OtLS0dPsxCkpUUI1QMOITKK1X5qAvrkCaOtxo17DRl/D3zC/jV5xmk4SyIgcA6ucTKrUa9fARFBlkXpdoXhjtVPbeGJ6bhowkCzrdXuytir0IdV+IkvVzRuXCFNTwMclqxtwxPAL0oYHTXfURRHyMMqhUzeGkPREen6qWLk2P50ZDceHzs5/9DFdffTXGjh0Lq9WKqVOnYtGiRbj66qsBAFVV/MuTn5/f7Xn5+fnyYz1ZunQpHA6H/FNcXKz0siNGCSNxsMcn2nByNEbr3khPsiIjiTf60uqEVtnciW0VTZAkYP74wOdk0hAufLZTZVdIaNW1WVBgkO7NItWlRsTHZJIwpST+0l1yGfuYQac8Nm88v/j4cJcxuzh7fQyNHZGnupo63HB6lKmojYQDGpSyCzJTbLIBXKlimlhEceHz+uuv429/+xtWrVqFLVu24KWXXsLvfvc7vPTSS9226xnSY4z1GeZ76KGH0NzcLP9UVFQoveyIOaJAWH2of1hpqwLDSpUsZRdo7fNZ7T/ATivJ6tZ4b5K/cy5FfEJDq4ouQZEwNxsk4qOG8AEgd3COl0aGNS1d2FPZAkkCzh6Ze8rj547Ng8Uk4WBNm6KNVpWiqcMFYfvLCqNTfWaKFTZ/CljPyq6DKg8n7Qn5fFQQPj/96U/x4IMP4qqrrsLEiRNx/fXX48c//jGWLl0KACgo4FcPPaM7NTU1p0SBBHa7HRkZGd1+jEI0PXwEdosZxf5Ga9H6fAJT2ZUXPlpV64g014V+Y6Vgsj/is+dkS9wPilQCYdocpHIPH0Gh/3NS3dIFn44GdNHDR8nmhcGIgaXx0sjw0wM8zTVxsKPXBoCOZCvOHJEDIHBRYiTEuIrMFCusYXjZJEkKamKoj/Dx+pgsJtUuZRfIwieBfT6KC5+Ojg6YTN1f1mw2y+XspaWlKCgowJo1a+THXS4X1q9fj1mzZim9HFVpC4rQRBthEbnXaK+ojkQxPqMvtOzl09Duwpfl9QB4GXswJdkpfFCk14e9VcbxeRkVrSM+eel2mCTA7WWoa9fnRNLl9sr7rWTzwmBEZdeR+g5DNGuMluBuzX0xT67uMp7PR/b3RFC5mKtzE8OKhg64PD7YLSYMUbj1Ql9QxEcF4XPppZfiN7/5Dd577z0cOXIEb731FpYtW4bvfOc7ALjKXrRoEZYsWYK33noLO3fuxE033YSUlBRcc801Si9HVYSfJifVhowoK6hGKNDLp6nDhaYONwDlPD5AcKpLfeHz0e5q+BgwoShDjoIJJEnCxMFkcA6VWn/EJ1+jiI/VbJKvoPXy+YgJ1+l2C7IibC8xEI4Uq1yQEOs+H6+P4bMDIQgfv9du67Em2TtmFOSKrjDGVQjydI74iDTXiEFpMJvUregSBMYkkfBRjKeffhpXXHEF7rzzTowbNw73338/br/9dvzqV7+St3nggQewaNEi3HnnnZgxYwZOnDiB1atXIz1dvXbdaiBSCUIYRINscI6ilbhQ8AUZSYr2L9FybIWYzdUzzSUQ/Xy2UyPDARGRjzyNIj5AcEm7Pj4fuaIrJ0XV0uB4GVi6/XgTmjrcSE+yYIrfu9Qb+RlJ8uNrdhsr3RVNxEcIn1qdxJxWHZuDKQ2qIjZybyY1UVz4pKenY/ny5Th69Cg6Oztx6NAh/PrXv4bNFvhQSpKExYsXo7KyEl1dXVi/fj3KysqUXorqiC9cOG3S+0JcQUaT6gpMZVc2ZBrw+Kh7cGjtcuNzv9+gZ5pLEKjsoohPf3g17tosCJS063MiUbOiKxgxsPTro8Ye5TAQooz97JG5A/Z6Es0MjVbWLsZVhFPRJZB7+egU8RFT2bUyNgM8GyBJQEuXJy5StZFAs7qiQJRQKiF8lBhWWl4XvdG6N4qCmhiqaVpdu68WLq8Pwwel9nkFNNl/1XmgphUdrsTtQzEQDf6uzSaNujYL9G5ieMwv/numSZXm9FI+O27rsSbFhgvrwfr9NQD6T3MJ5k3g6a4vDtWjudOt6rrCoT6CcRUCvc3NWkxl70mS1Ywi//c0UX0+JHyiQHzhlBA+uWk2pPuHlYpu0OFSrnAPH0F+sGlVxWZfHwZVc/WVpsjPSEJ+hh0+Buw8QQbnvhA+jByNujYL9J7XJUd8VGheGMyIQanITbPD6fHF7Py4pg4XtvnXfk4IwmfEoDSMzEuDx8ewbl+NyqsLnUiaFwrydDQ3M8aCprJrJ3yAoDFJJHyIcGlUUPhIkhQ0uiKydNcRFUrZAcBiNqHAny5Ry+Dc5fZirf9g2leaSyD7fKiRYZ8I/5lWFV0C/SM+2qS6JEnCGcN51Gfj4dhMd31+sA4+xtMsofoUhcn54z0GEj4RjKsQBAaVav95PdnchQ6XFxaTpFoFYl8kemUXCZ8oaFBQ+ADRDStljKkmfAD1fT6f7q9Fh8uLIkeSXLnVF5OpkeGAiPlDWlV0CQqFx0eHiI/Xx1DRKMZVqF8afMZw3ttm4+F61d9LDT4NoYy9J5P8Fx3i92wE5FRXFB6fujaX5sOPD1Rzf09pbmpY/YeUINF7+ZDwiQLxhQunW2h/RFPSXt/uQqvTA0lSx9+gdhPDT/0ltfP6SXMJaHTFwOhR0QVA9g5Utzo1P5FUNnfC7WWwmiVFKi0HQgifLccadR15EAmMsX7HVPSFEBdGMsVGU9WVk2aDJHHRrPU+6ZXmAijiE9PC5z87KnV9/8YorjR6I5rKLhHtKXIkI8mqXCm7QO1ePuIgIERNf0wanAmAe6GaOoxzADYS1f7QfZ7GEZ9B6XaYTRKvKtPYNyHSXEOyUjTpidLd5xNb0cfDde2obnHCbjHhtGHZIT9PRLcb2ozxvXN7fbLROpI+PlazCdn+C1etP69aj6oIZnguf8/y+nZdu6zrRUwLnxanvpUFSkd8ohlWqsaoimAGq9y9WYzqGBHChGJHihXD/B4OKmvvHTnVpWEpOwCYTRLy/YZRrWd2CWOzFmkuoKfPJ7bSXbtP8sKA8UUZYV0oiahKq9NjiLEx4uLTJAGZyZE1rAxUdmnr8xHH7OEaTGXvyeCsZFjNElwen+6z9fQgpoVPl0u/8LLb60NrFy+nVqpcuNuw0jCrp9Tq4SOQU10qfEmaO93y1ZbwOQ3EJDI490uNHPHRNtUFBGZ2ad29+ahGxuZgYtXns7uSC59xheHNPcxIssrRtEYDRFvr5F5qdpgijPKJQchal7Qfb+THUrVbL/SGOchQHc20gFglpoVPp0u/K47gKw1HhFcaPek2rLQmvA9jIOKjztWDmuZmYebOz7AjPcTRHyIlti3GUgxaoVfEBwiUtGvdvfmYysNJe0NEfDYfjS2fjxzxCVP4mEySPAqk3gDprkBFV+QXn3qUtLu9Pvn7UZylvh+tN4YnsM8npoVPh46Nwxo6AmmuSK80eiMwRyU8n8/+ar59qcoRn4Z2FzoVjrSFk+YSiMouivicSnDXZq3NzUBww0ttIz7H5K7N2pUGjxiUhtw0W8z5fPZUBlJd4SL7fAxgcI6mh49gkA7C52RTJ3wMsFsC8+20RoyuIOETY3Tp2LlXmPuyFO6KO7qAzysLpylaTWsXDta0QZICM4SUJiPJgjS7BYDy6S5h5g41zQXwIaYmiYenq3TqGWNU6tudunRtFoieT5pHfOq19fgA3OczM8bSXbWtTtS0OiFJwNiC8OcjysLHAKmuQCl75OIhTwePj0hzDclKVnWmXH8k8rDSmBY+HTp6fBoUHFcRzKwRuQCAzw/UhWxw3nCQH3AnFGUgUyGjdU8kSZLnMCltcBaprnAiPik2C0bn+0UiRX26IdJcuRp3bRYEPifanUjanR60+D134v21ItZ8PiLaU5qTihSbJeznByq79BnzEIw8pyuqVJff49Oi3f5U+KOTevh7BMIWUR7FYOxYJaaFj54zcuTmhQoLjdOHZcNmNuFkc1fISvy/B/mgwbP8okktilQyrUaS6gKon09fyMZmHdJcQKB7s5aROBFdSrdbQvaJKcWZMebzkY3NEaS5AIOmuqIRPnL3Zg2FT6NovaCPvwcIVAAfb+yMic+tksS08Ok0gvBRqIePINlmxvShPF0lJpX3B2MMGw7xK81ZI7URPkr28nF7fTjqr0gbEWY/i0BlV+x4K7SgWqeuzQLRvbmmtQserzYFCCK6VODQfp9jzecTqbFZkO0fDREvqa5BaYFUV7htRCKlokEYm/WL+OSm2ZBu5/Mhj0U4HzJWiW3ho2NVl1oRHwA4e5Q/3XVwYOFztL4DJ5o6YTVLOG2YOv4ewWAVujdXNHTA7WVItppRGGYF0uQg4aPVASsWqJG7NusjfHJT7bCaJfgY7+CsBSK6VKhBx+aexJrPRzY2Ryh8cowU8fFXdUVjORARny63D21ObXyjxxv1T3VJkiQbnBPN5xPTwkfXqi6F53QFM9svfDYeqh/wivm/h7g4mlqSFVG+Phxk74aCplWR5ho+KDXs6rgxBemwmU1o7nRHPNE+HqnWsYcPwEueRRm9VjO7xGeySIeIDxDw+XxZbmzh0+X2ysUEkVR0AYGCDkOUs/vXkBtF5D3FFijc0CrdVdGof8QHCKS7Eq2XT0wLny63jlVdKgqfCUUOZKZY0er0DGjcFcZmtf09QGAOk5Km1cNyRVf4/YdsFhPG+KtS9vsH/hFATYuYzK6PCAACnxWtStqF70z4i7TmjNLY8Pnsq2qFj/GoTaTC2EgRnwYFUl1AUGWXBgbnLrdXLp3X0+MDBI2uSDCDc0wLH12rulQUPmaThFkj+BXkZ/34fHw+hg3+iM9ZI3MUX0dPgj0+SqWWDskVXZH1XhEHDq17xhgZcdWar5O5GQia0q5RSbuI+BRqXNElGJmXhpxUG7rcPkN7znYH9e+JtIxaHPP07tzc5fbKqalo5yVqObZCpLnS7BZkpmhrxO9JovbyiWnh44zTVBcAnD2ST0zuz+C8p6oFjR1upNrMckM/NSlwJEGSAJfHJ5sKoyXSii5BoUN531GsU92iz4DSYITJWKuSdiF8i3SK+PC5XX6fzyHjprv2RDiqIpiA8HHrOuBSHIOsZgnp9ujS/Fo2MawwQA8fQaJ2b45p4aOXx4cxJl/tqCV8hM9na0UTWrt6H8Yq0lynl2bDqkG/FqvZJIeElRAajDF5QnGkwkf4jtSaGh9r+HxMnl+kV0dYIDjVpf7fhTEme4n0qOoSyANLDezzibaiCwgMZfb6mDwZXQ8CPXzsUQsIcZGghfA5boAePoJhfuFT1+bS9W+pNTEtfLrcPl2qeVqdHri9/H3VEj7F2SkYmpMCr4/hy8MNvW7zXznNpb6/R1CkYGVXQzv/sklS5FPl9RqPYFSaOt3w+q/Cow3/R0NgXpf6f5dWpwft/rS31s0LgxERH6P6fHw+FtWoCoHNYkJ6Eo+wKBX5jYRAKXv0n3Mte/kYxdgM8HSbuJg9kkBRn5gWPoxx8aM1YkBpis2MJKtZtfcRgqa3snaXx4evyrkgmqWBsVkQ8PlEf0ITaa7BmclItkX2e1RSiMUDdf6r4MwUqyZRwL7QUpAKY7Mj2ap6ZWN/GN3nc6yhA+0uL2wWk5ziiJQcA/h86tuUi7prObZCdG3W29gsKNU43fXpgVpN3qc/Ylr4AECHDvO6xJVGlkrjIQSz/cLns14+KN8cb0KHy4vsVFtE83YiRclePtFUdAnEFX51i3bN8oxMXWtgXIWeiJRTXZsTLo+6fxfZ2Kxjmgswvs9HRHvG5KdHPcrECCXtItWlxGddS4+PmNNlhFQXEJiRqFUvn398XaHJ+/RHzAsfPbo3NyoYYu2PWSNyYZJ4ZKSnV0KMqThzRI6i0+EHokg2rUYvfKKt6AL0aZZnZMRU9kE6C5+cVBtsFhMYC5it1UJEfIp0aF7YEyP7fHZH2bgwGCOUtMul7IpEfES3cS1SXcLjo//nFQiUtIsLUbUxwlDp2Bc+OpS0axXxcaRYMdHfnbhndZeW/XuCUTK1FG1FF8Cb5VFlVwBhbM7V0dgM8OhHoYIiuT8qDRLxAbr7fNSOdIWLbGyOwt8jCMzr0u9iQ3zWlRgbJFJdTR1uVf1ZrV1uNHVwE/EQA3h8AO2bGNaofCEUCjEvfPTo5dOo4JXGQJzt788T7PPpcHmwtaIRgDb9e4JR1uMTXUWXQKsTbCxQJ4f/9TM2C8TfpUrtiI8YV2EA4dPd59Ok93K6sVsBY7MgSxY+OlZ1+UVXbmr0Ip974njkXM10l5jRlZVilbtF683woF4+arcncHq8qNfxMyMg4RMBIsSapYnw4f18/nuwTv5QflXeALeXYXBmMko0zhMLj09dmxNdUaQZu9xe2eQ3Ii86o2XAd6T/lYTeGMXjAwT3WFJb+IiIj/6pAz63i6e7vjCQz6ex3SULRCU8gTkGiPg0KGg5kCQpaFipisLHADO6elKcnQKrWUKn26v6RYoR0lxAHAifTh3GVqjdvDCYaUMzkWw1o67Nhb1VfCyDmMZ+1sgczRtgZaZYkeyvZIvmQ3y0vgM+BqQnWaL2owQqiCjiU2cQjw8QXNKucqpLjKvQsZQ9GFFl+dHeGp1XEkAYm0uyU5CeFH23YDGhXddy9jZlxlUIBmWo38vnuIFK2QVWswlDc/jF5yGVfT5GaTsS88JHz4iPFsLHbjHLV5DC0Cz+1bJ/j0CSpMCw0ihSS4eD0lzRirdCBdYTLwhzc266/qkuOS3aqN7fhTEWNKBU/4gPAMybkA9JAr6paJLHE+iNksZmwBjl7E3+985MVmbsQ6CkXc1Ul7+U3SDGZoEoMDlUo7bwMcYxOuaFjx7m5gaVuzb35GxR1n6wDo3tLvkgduYIbf09guCZXZFySC5ljy7N1X09xria0JO6VjGtWv+Ij+hTomZX7aYOt9zLS8+uzcHkpSfhtGH8YuWDnVU6r4ajpLEZCPL46FTO7vUxuWmlaKYYLUL41KqY7hFC2EgRHyDgszykssGZIj4KoUc5u5YRHwA42z++4qvyeqzfXwvGgNH5abrNYlLCU6NERZdAy/EIRoYxFjB8Gkn4qBjxEQfSnFSbqs1Ew+WSiYUAgPd3VOq8Es5uBWZ0BSMiPvXtLl2657d1BSwOSqTuAG1K2oW52SjNCwUB4aNyxMcgF6cxL3x0SXUp2DE0FMbkp2NQuh1dbh9WrD0IQNtuzT1RoqRdqYouvh5+wGrqcOvS0NIoNHe65VEqeo6rEIjPSavTo9ocICF2jRLtEVxYVgBJArYca9JdkDs9XnkmnlIRH3Hsc3p8uhyDW/zzC+0WE2wWZU5jajcxZIwFIj4GMjcDwIg8jYQPRXyUQesvncvjQ6uTn1yzVe7jI5AkSU53iQOYHv4egSx8IjygM8bkXPLIKCu6AH7FJ8LdiVzZJYzNGUkW2C36Rz9SbBb5BKmW1+WkXMpurCvo/IwkzBiaBQD4zw59010Hqtvg8TE4kq1yA9JoSbGZYfcLDj2aGLb6Iz5KRXsA9T0+jR1uOT032ADNNoMRloPqFmefQ7GVQO+LAEHMC59Oja/whZnPbJLgUMhUFwrBQsckQTY864H40h6tj+xkVtPqRLvLC7NJQkl29MIHCKS7EtngXNtqjOaFwaid7hJT2fUcTtoXF5XxdNd/duqb7gqkudIVqwKVJCmoiaEewoefnDMU8vcAgaihWiXdwticn2E3VFoWADKSrLLwU7ORIZWzK4TWER+5h0+KVdNREWcHCZ9JQzKRoeCVTriIPiDHGjrkkHM4iGjP0OwUxcLUSlSaxTpGKmUXDFbACN8flQaN+ADARRMLAABfH21UfWxHf8jG5kKHoq+rr/ARER/lhY9a8+VEDx+jdGzuido+ny63V9f2B8HEvPDR2tzcoNG4ip4UOJIwyp+H1bpbc0+yUm3yCU0cVMNByYouQaGcfjPGFYUeyF2bDRTxGaxySftJA0d8Ch3JmFaSCcb0re7ao2DH5mCygwzOWtPq5BdcSqa6slNssJnVmy8X6OFjPJEOBBrJqiV8RLTHbtVfdui/gijRupxd64quYH503ihMLcnE1aeXaP7ePSkbzA+iO080h/1cJSu6BEpOjY9VjBjxEamu4yoJH5GWMGLEBwAu1rm6izGmeA8fgTgGNuoY8VFy7IPJJKma7hKpLqMZmwVyxKdGnVSXHJ3N0P8iJeaFj16pLj2Ez6WTi/DWnWcZIlRaVsTD5ruiiPgoKXy06hJsZAI9fPSv6BIM9n9W1Uh1McYMNaerNy7yC5+vjjSgplX7aOTxxk60dnlgNUsYmafc9w3QOeKjQqoLCKS71Kg+qjBg1+Zg1E51iWNzPgmf6EmkiI+RmBBNxMfv8Yl2RlcwRTSvK9C12UARHzU9PvXtLrg8PkiSMQ6mvTE4MxmTi3m668Nd1Zq/v4j2jMxLV8xPJ9BzXpcaVV1A0GBdFS6gjouuzYZNdXHhc6S+HR6v8h4nISaN8F2NeeHTofGsLhI+HBHxOVTbFpb47HB5ZB/O8Fx1Ul16NFQzAnVGFD7+g3xDu0vxHkuiGVpuml3xk7qSXOI3Ob+/Xft01x6V0lxAYF6XHhPaRVWXWhEfpS+gfD6G437xb9RUV2FGEpKtZri9TI5OKYncc4uET/RoHvHReFyFUcnLSMKgdDt8DNhTFXq6S5RK5qTaFJ1un5+RBEniDdX0qDIxAvJkdgOZmx3JgR5LShucAzO69D+Q9ocoa/+yvF4Wp1qh9KiKYLJTebRF34iPssJHtMVQuuy61l8pZjZJhk3LmkySXHCixswu8TvNz9T/+ETCJ0y07tpsZMr8B9NdYaS71KjoAgCbxSSbehMx3cUYQ12b8Tw+QCAad1zhdFeVgUvZgynOTsGkIQ74GLBa43RXcA8fpQlEfPTs46Nsqkv2+ChsbhbG5kJHEixm45521fT5iOMyRXwUoEPjcvZGivjIlA3m6a6dJ0KP+KhR0SUojLKjdCzT0uWBy5+XN1KqC1CviaH4OxcasJS9JyLqo2V1V3VLl1xNp06qK/7MzXKRhMIivcKgw0l7oqbwEZVyJHwUQOuqrnqd+vgYkQn+iM/Ok+FHfNQQPoMTuImhSKGk2y2G6worR3wUFj7C42PU1EEwF/t9Pl8crtcsQrJ6F+8dNLUkE5kqHK+Eubm1ywO3CmbY/lDP3Mw/q7VtTkX36bhBh5P2JNDLR9mS9i63V/7cF2To/zuIeeHj8vjg9WljZmWMyT0rjDAEUm8m+A3O+6tbQ+50Kjw+SlZ0CQJT2hMv1SX8PYMM5O8RDFGppF2YJY2e6gKAoTmpmFCUAa+PyYJEbUQV2YUTClR5fUeyFaJ5vda9fNQyN+ek2mA1S2BM2ZldFQYdTtoTcUF6sKZN0SIRkZZOtpqRkazs3ywSYl74ANBsIndLlwcev8iiiA+/enEkW+H2Muyvbh1we5+P4bCKEZ9ClccjGJmAv8d4wmewnOpSdlCp8AwYsWtzb8jNDDXo4tzU4cIXh+sBAPNVEj4mkyQfB7VOd6mV6jKZJLncWsmS9ooGUdFlbJFempsKSQKaO92KRiaD09JKzYuLhpgWPuL3p9XYCnFVk2ozGy6doAeSJMkdnHeFkO460dQJp8cHm9mkShNGSnUBuenGE+Rq9PLx+Zg8ViAWIj4AcFEZFyAbDtahqUNdofDxnhp4fQxjC9IxLFf56KpAj3ldPh9Dm0udVBcQSJ0qWSQRKx6fJKtZTscpme4yWlo6poVPsn/mh1aVXbK/h4zNMiLdFYrBWfh7huWmwKzCgFdxAqxMwKouI/bwEYgDaXWLE06PMt/VujYnPD4GkwR5qrTRGT4oDWML0uHxMdVnd33gT6fNUynaI8jSQfi0uzwQWRilIz5A4DiiVEm7x+uT0+9G6Lo/EGoYnI02WibGhQ+PumhlcJb9PSR8ZMIxOIt0mJKNC4MR3ZurW7s0N1vqjZGFT3aqDUn+ixSlROnJoC6wRi4P7snlUwcDAP7383L4VPImdrg8+HR/LQD1/D2CHB2Ej0hzWc0S7Co0rixUeGxFZXMXvD4Gm8UUEyI9MLNLOeEjovAU8VGAJJu2wqeBIj6nIEra91S2DNjmXPQwOa00W5W15KTaYLOoN13ZyNS2Glf4SJKkeLqr0mAH0lC5ZmYJ0pMsOFjThg9VMjl/ur8WTo8PxdnJqvTvCUaPkvbgii41/CIFCs/9E2muIZnJMKkQ6VYaVSI+Buu5FdPCJ8XKw5xapbqoa/OplOakItVmRpfbh8N1feeETzR14uujjZAk4BK/yVNpTEFdUROtiWGtQZsXCsSw0uMKGZxPGuxAGioZSVZ8f9YwAMDTnxxUZbyKSKPNH1+gupE0R4cJ7WpVdAkKFa4OlUvZDV7RJRgxSPmSdvn7apBChJgWPklyqkubqi55ThdVdMmYTJLcDr8/g/N7208CAE4fli1fUamBKGlPNIOzEcdVBKN0E8NYjfgAwPfPKkWKzYzdlS1Yu69G0dd2eXz4eC9/zQvL1E1zAfp4fNSq6BIEBpUqJHxExMfgPXwEYlhpRWMHuhQqHKpqNtb3NaaFT4o/1aVVVZcsfAx6Va0XoRic//0N71h76eQiVdcirigSqXszH1fh7+NjwFQXoPzYCjFSQLQwiCWyUm24/oyhAIA/fqxs1OeLw/Vo7fIgN82OaSVZir1uXwRSXdrN62oRER+78hVdQODkXNPapciUcjHw0+gVXYKcVBsykixgjE9qj5ZOlxeNHfxvZpQIbUwLn2SbtlVdFPHpHdng3MfMrvK6duw40QyzSZJLetUieEp7otDm9MDpMea4CoFaER+jDyjti5tnl8JuMWFbRRM2HKpX7HU/lKu58jXxk+ToMK9L7YhPTpodFpMEn0JNDMWcLqP38BFIkiRHfQ7VRC98REVXis2MDJX+ZuES08InyaKPuZk8Pt0RBufdJ1t6rVR59xue5jprZC5yVD4xi8quRCppF80L0+wWJNuM2V9KCB+lxlZUNsduxAcA8tKTcPXpJQCApz85oMhr8q7QvIBAraaFPQn08XFr8n6AeuMqBOagJoZK+HxipYdPMEoanIPT0kZoXgioJHxOnDiB6667Djk5OUhJScGUKVOwefNm+XHGGBYvXoyioiIkJydj7ty52LVrV9jvk6xXqouETzdG5qXBZjGh1enBsYZTzav/9vt7Lp2kjqk5GBGmTqTuzYFSduN+Lgdn8oN+VUv06QOP1ydX7cVqxAcAbjtnOKxmCRsPN+DrIw1Rv97WY42oa3MiPcmCM4fnKLDCgRHHwsYOl2rl+T1R29wMKOfzcXq8qG7h30+jj6sIRknhY8RCBMWFT2NjI8466yxYrVb85z//we7du/HUU08hMzNT3ubJJ5/EsmXLsGLFCmzatAkFBQW44IIL0No68NiDYFJs/IOvlbm5kYRPr1jNJowr4GWzu0529/nsq2rF/uo22Mwm1ZupAYmZ6qozcCm7IC/dDqtZgtfHUB1l+qCm1QkfAywmSfUIopoUZSbjiulDAAAr1h6M+vVEmuu8sXmwqdDfpjeyUnnUxetjsvdGbUTER820iVIl7SK1m2IzIytFnQiVGgQqu6IXPkYzNgMqCJ8nnngCxcXFWLlyJU4//XQMGzYM5513HkaMGAGAR3uWL1+Ohx9+GAsXLkRZWRleeukldHR0YNWqVWG9l6jq6nSp36zO6fGi1cm/cCR8TmW8MDj3qOz6tz/NNWfMIDiS1f/ii9RHS5cHbU5tBLHeGLl5oYC3GlDG51MZ1LxQjQ7gWvLDOSNhNklYt68WO44P3AS0LxhjcrdmLaq5BHaLGel2LkC06uUTiPiodzyRU+ZRRnyCjc1GSfOEQrDHJ9pI3kkDpqUVFz7vvPMOZsyYge9+97vIy8vD1KlT8cILL8iPl5eXo6qqCvPmzZPvs9vtmDNnDjZs2NDrazqdTrS0tHT7AQKdmzvd6p/gmvyudLNJQoaKX7hYRczsCjY4M8YCaS6Vq7kEaXaLfCVYmSBRH7mHjwHndAUT8PlE18tHXIXHynDS/ijJScFl/u/GirWRe332VLaioqETdosJ54wepNTyQiJL414+4oJGzVRXQYYyqa5YMzYLSrJTYDFJ6HR7ZXNypASaFxrn+6q48Dl8+DCeffZZjBo1Ch9++CHuuOMO/OhHP8Jf//pXAEBVFb8qyc/P7/a8/Px8+bGeLF26FA6HQ/4pLi4GEKjq0sLcXO8/uWSlWGOi+6bWlPkjPrtOtsjluTtONONofQeSrWacPy5Ps7WIq7WTCvXhMDpG7tocjNy9OdqIT5PxPAPRcOe5IyBJwIe7qrGvKrx0v0Ckuc4ZPUi2AGiF1t2bW/yprjQNPD7Rprrkrs0xZGwGuH1haA5fc7TpLqONqwBUED4+nw/Tpk3DkiVLMHXqVNx+++249dZb8eyzz3bbrmfYjzHWZyjwoYceQnNzs/xTUVEBQNuRFWRs7p8xBekwmyQ0tLvk8LBIc503Lk/Tg3FRgvl8YiHVBQCDRUl7lH8X0aPJKF1go2VkXjouLuPG/z9F6PURwkft2Vy9ofW8LrWruoBgj090F0+iHHxYTmwJH0C5mV1GG1AKqCB8CgsLMX78+G73jRs3DseOHQMAFBTwL2bP6E5NTc0pUSCB3W5HRkZGtx8ASJE9PhoInw4R8SHh0xtJVjNG+fPCO080w+djeHe7Nk0LeyJSICR8jMUQeWyFMhGfIgMdSKPlznO5B/Ld7Sexp7LvRqC9caSuHXurWmE2SThPw8iqIFtz4aN+VZe4eKppdUZVhbivmv8txxRkKLIuLZF9PlGMruh0eWWbiJEuVBQXPmeddRb27dvX7b79+/dj6FDeqbS0tBQFBQVYs2aN/LjL5cL69esxa9assN4ryapdVVeD/+SSY+CSYb2ZEJTu2nysEZXNXUi3WzBHY89BoTy2IjFSXXLXZoN7fJQaVCq6Nqs5+kRrJhQ5cP64fPgYcP2LX2J/degpLxHtOXN4DjJ1uDDTXvioX9WVm2aH2cSrEEWfrHBpd3pQ4Z/TNaZA3WGxaqBESbtIFabaAiZ4I6C48Pnxj3+MjRs3YsmSJTh48CBWrVqF559/HnfddRcAnuJatGgRlixZgrfeegs7d+7ETTfdhJSUFFxzzTVhvVdgZIX6VV0NftVKEZ++EQbnXSeb5TTXvAkFcvWdViRaSXtdqxhQavSIT0D4RFMpEujaHD8RHwB48opJGF+Ygbo2F656fmNIkZ+a1i78Y/NxAMD8Cb1HzNVGS+HDGAsyN6uX6jKbJOT7595F6vMR4nVQuj0mLRJKlLQHNxo1UlWb4sLntNNOw1tvvYVXX30VZWVl+NWvfoXly5fj2muvlbd54IEHsGjRItx5552YMWMGTpw4gdWrVyM9PTxVnGwVIys0iPj4Z9HkxOAHWCtEB+ftx5vx/g6R5lK/aWFPAqWo8S982p0euYGn0YVPgSMJJokP0qyLcLaTy+NDrT/CZaTQuRJkp9qw6taZmDjYgYZ2F655YWO/g38/2VuNi5Z/hoM1bUi3W3BhmfbfNUBbc3OHywuvXzSrmeoCAuXXkfp8hPAZG4PRHgAY7o/4VLc45fRiuBjR2Ayo1Ln529/+Nnbs2IGuri7s2bMHt956a7fHJUnC4sWLUVlZia6uLqxfvx5lZWVhv09gOrv6Hp9Gf0v2LBI+fTKuMAOSxPPidW0uZKVYcdbIXM3XIb5kJ5u7NOsmqxcizZViMyPVQKHk3rCaTXKZcKQ+n+qWLjAG2CymuLwIyUyx4W+3zMTk4kw0drhxzQtfntLfp8vtxf/710784C9fo77dhbEF6XjjzlkYlK6P8M3WsJxdpLnMJkluZ6IW0Rqc9/or9Ebnx6bwcSRb5c9UeV1kPh8jlrIDMT6rSx5ZoUU5u/8KNRZDllqRZregNDdVvn3RxEJYzdp/xAocSZD8kQWtSmz1IlaMzYLBUQ4rrQw6kBopdK4kjmQrXr75dEwryURzpxvX/O9GbKtoAgDsqWzBpU9/jr9+cRQAcPPZpXj7rrN0PblqmeoKNjar/fcvlHv5RJfqikV/jyDadJcRx1UAMS58UoS52e2Ve8eohYj4kPDpH2FwBoBLJ2lbzSWwmk3IizI/HyvUyv6e2PhcRmtwrjRg+3s1yEiy4q83z8Rpw7LQ2uXB9f/7JZb+Zw8uW/FfHKhpw6B0O176wen4xbfHa+6h64mY0F4fYfoyHFpUnsweTEFQ5DgSRE+mMTEa8QGCS9ojjfgY8/sa08JH9PHx+hjcXnWFTz318QmJsiJucM5Lt+P00mzd1pEovXxiLeITKGmPrHtzpUGvINUgzW7BX75/OmaWZqPV6cFz6w/D5fXh/HF5+ODe2ZpXS/ZFtl90d7l9qlfYysZmu/rd88UxJJLuzXVtPN0vScCo/DSll6YZQviEU2UYTKUBx1UAMS58RFUXoG66izGGxg4SPqFw2ZTBmFyciZ/OH6PrHKWiBClpl7s26+TvCJeoU10GNUuqRardgpXfPw3njhmEVJsZv7q8DC/cMMNQw1lTbWbY/ClttdNdWvTwERREMaF9vz/aU5KdonknbSWZNIRH8DcdaYjIL1lpUI9P7P5FwFMaVrMEt5ehw+2BA+pcBbR0euRKAipn758CRxL+dddZei8jYZoYxlrEJ9pUlxEHHqpNis2Cld8/HR6vDxYdPHMDIUkSslNtqGrpQkO7S9XxDK0aprrEybq6pQteHwvrQm5fdeynuQBgcnEm0u0WNHa4setkCyYOcQz8JD8dLg+aO/3NCw0mfIz3LQoTLSq7RNfmVJtZ93w6ERqBeV3aCJ+Khg5c+dwXeHvrCU3eTyA3L4wVj09QxCcSX56oBisy2IFUC4woegRalbRrMZldkJeeBLNJgsfH5O9ZqMj+nhg2NgM8uHDGiBwAwKcHasN6roj2pNstmvy9wsG436QQSdGgskv08MmOkZMLoX335t9+uA9fljfgDx9HPmE7EkRX2ViL+LQHtbIPlS63FwfioFImHhEd7Rsi7HIcKlpGfMwmKahIIrzjiIj4xGopezCzR/GWJJ8fqAvreWK0jBE7rMeB8BFjK9QUPv6KLkpzxQxadm8+WNOKf2/nnarL69pxrD4y424kyKmuGPH4JFnNskgLN921p7IFHh9DdqpN/vsSxkBYAIQXUi20FD5AsM8n9M+qz8dkj0+sNi8MZvYobqLffLQxLPN6YJiw8b6rMS98RBMr0b1WDRqoh0/MIbr61rY54fKoO9Lk6U8OIjhrsz7MkHA01LXGlscHCKS7wm1iuOMEb+Q3aYgjbnv4xCpapbpaNEx1AQFvSjgRnxNNnWh3eWE1SxgW1NcsVhmWk4LBmclweX34srwh5OfJzQszKOKjOIFUl3pllA3UtTnmyEm1wWYxgbHIqjJC5WBNG97xzyW7ZBIfGbB+nzbCp9PlRbs/0qlX195ICJ7ZFQ7fVPiFz+DQDZaENogu2vGU6gICKfNwhI/w94wYlKZLA1elkSQponSX3HPLgKNlYv6vIro3q5vqojldsYYkSRiazatLDtdFPmRvIJ7+5AAYA+aNz8cP54wAAHxxqE71KBMQSHMlWU1ItcWO6X5Ipoj4hJcS3HGiCQAwaUimwisiokVcFDaonuoyfsRnXxz60ES667MwotlGLWUH4kH4aFHVRRGfmEQ0DjtYo47wOVjTJk+h/9F5ozC+MAO5aTa0u7zYfLRRlfcMpjaolD2WUj+R9PJpd3rkv+OkMEpqCW3I0WhsRSx4fOKloiuYWSNyIEnA/uo2VLeEJgKFudmIzUZjXvhoWdVFEZ/YYuQgdYXPik8OwMeAC8bno2ywAyaThHP8V0br96uf7opFfw8QWS+fXSdb4GNAQUYS8gzoGUh0tJrXJYRPhsaprnCqQ/fHSQ+fYLJSbXKK+bMQ010i1VVEqS7lSdaiqstfdkvNC2OLkf4DjxrC51BtwNtz73mj5PvP8Y8R+FQD4VMbY80LBYGxFaELn+3HmwBQtMeoiHL2+jD73YSLPLJC41RXdUtXSJ2L3V6fPNAzniI+AHC27PMZ+NjW7vTIc9UKKOKjPHLER4Oqrhzq4xNTiIjPgZo2xYfYrvjkIHwMOH8cj/YIZo/KhSQBuytbUNOqbg+hOv+A0kHpsfW5FKmu5k63fCIbiG+OByq6COMhLgpbujxwe9XxtzHGNB1ZAfCiAZME3sQwhCGs5XXtcHsZ0uyWuGu5cPZIflH3+cG6AUWg3LwwyYI0u/EGRMSP8FGxqktMZqeIT2wxfFAqTBI/wdYpWG1yqLYN/9rGOzQvOn9Ut8dy0uwo80+o/2x/eA2/wiXWxlUI0uwWOJL5FXuoPp8dcsQnU6VVEdGQmWKDsJmp1cvH6fHJw6i1ivhYzSa5YjKU6tC9VaJxYVpM+e5CYdrQTKTYzKhrc8n72ReVBp3KLoh54aP2yAqnxytfleakxtYJJtFJsppR7K/sOlAT2XTh3ugr2iMQU7PV9vnEqvABgn0+A1d2NXe4ccTfFJIiPsbEbJLkC0O1fD6ih48kASkajg4Kx+ezPw6NzQK7xYyZpdkABq7uMrKxGYgD4SMiPh0qpbpEtMdskjQLrxLKMSqPp7sOKeTzORwU7Qn29gQjfD6fHaiVh9uqQSwLnyFhNDEUjQtLslOQSVFXw5Ktci8fYWxOs1tgCmNgaLQUhlHZJSIh8WRsDkaUtX9+sP9otpFL2YE4Ej5qVXXV+/O6WSk2Tb9shDKMyAv4fJQgEO3J63NS8dSSwETjnf6TthoE5nTFnhgoHcQ72m4Joez/GzI2xwRipI9avXwCFV3aDrwUJe2VIZRxi4qu0XEY8QECc7u+Km9AVx/Bhg6XR744NGrn6pgXPqKqSy3hI8K2VMoem4zKU66yq6KhA2/L0Z7RfW5nNZswaySfaKxmuksuZ4+hrs2CC8blAwA+3lMDp6f/7+4OMjbHBGqXtGttbBYUie7NA6S62p0eHGvgKdl4jfiMzEtDfoYdTo8Pm470Pr5iyft7cLiuHQUZSbj6tBKNVxgaMS98RK5XrVSX+BLTnK7YZKSCEZ91+2vhY8Dppdl9RnsEc0bnAVCvrL3L7UWr33sWS+MqBNNKspCfYUer0zNgG/ztZGyOCbLlknZ1Iz5aC59AE8P+hY84xuSm2ZETg+nnUODjK/zprl6+t2v31eBvG48BAJ763mQ4UrSNzoVK7Asflau6xJc4OwbTCQQwwp9SqW11otnfjylSvjxcDwA4e2TugNueM5pvs7WiCc2d0b1vbwh/j81iQroBy0UHwmSScFEZn232/o6qPrerbXXiZHMXJAm9GskJ45CtsrlZ63EVAnlsRUv/Hp99VS0AgDEFaaqvSU9EuuvTHsKnod2FB/65HQDwg7NKcVYIx0m9iHnhk6TyrK566toc06QnWeUD18HayCu7GGPYeJiHdkVlQ38MyUrBiEGp8PoYNgxgBIwE4e8ZFGPjKoK5qKwAALBmd1Wfs83EfK4Rg9IM2Q+ECCBG+qhVzq53xKe62dlv/5p9Vf7GhfkZmqxLL4Sg2VPZglp/up0xhofe3I7aVidG5aXhgQvH6LnEAYl54aO2uZlSXbGPSHdF4/M5XNeOujYn7BYTJhdnhvQcke5Sw+dTK4+riN3P5Yxh2chNs6Oly4MNh3oXh/JEdvL3GJ4sf1pDjQgnoJ/wyc9IgiQBLq8P9f1Es/ZVJ0bEJzfNjvGFXNz9139R98/Nx/HhrmpYzRJ+f+UUuc2MUYl94WNVd2SFSHVRxCd2kX0+1ZELn43+NNfUksyQv9Qi3fXp/lrFO0fHcim7wGyS5KjPf/pId4lS9kmU5jI8mX7ho37ER9tUl9VswqC0gZsYyhGfgviO+ACBdNdnB+pQ0dCBX/57NwDgxxeMjomUdMwLn+SgkRVKn1yA4IhP7J5gEh054lMbufD5Uk5z5YT8nDOG58BuMeFkc5fi88JidUBpTy6ayIXPh7urThl1wBgLGJtDjLIR+iF6LDVF6aXrC72quoAgn08fvXzq25zyxYjoHRbPCIPzZwdq8ZO/f4M2pwenDcvC7eeM0HlloRHzwkekugCgy638jBhKdcU+oqQ90ogPYwxflvOIzxnDQxc+SVYzTvf7gZROd8kRnxib09WT04dlIyfVhqYOtxxVE5xs7kJdmwsWkySH1gnjkukfQ6Ke8NEn4gMEVXb10ctnn79/T0l2ClITwIs2Y1gW7BYTalqd+OpIA1JtZiz73hSYY6TXXcwLn+SgtEOHCpVdIqdLA0pjFxHxOdHUGdFn5Eh9B6pbnLCZTZhakhnWc9UaXxFoXhjbER+L2YT5/nRXz+ouMZ9rdH664T0DRGCWYZtTnUGlrU5/xEcHYTHQ2Ip98oyu+Ozf05PgizoAeHTBBHk8UCwQ88LHZJJgt/DdUNrn4/b6ZKMeRXxil+xUm+zROlTTHvbzRRn7lOLQ/T0CIXy+LG9Q1IBfGwceH8HF/rL21buq4Ak6YYqJ7JOLje8ZIICMZKs8qFSNqI9e5mYgkOraU9nS6xga0bF5bJx2bO6NC/0XLBdOKMB3pw/ReTXhEfPCBwiq7FK4iaEw6UkSTWaPdUbIPp/wS9pFCuaM4QOXsfdkZF4aihxJcHl8crpMCU42ca9BgUFn4YTDzOHZyEqxor7dha/KA91gRcfmiYMzdVoZEQ5mkySPk2hSweCsZ6pLNCxdv78Wl//pv6eMopGnsieQ8Ln6tBK88cMz8fQ1U2OupUacCB91KruEvycz2RozuUuid0ZFWNnF/T1+Y3MY/h6BJElBQ0uV6efT5fbihF/4lBp0Fk44WM0mzBvvT3ftrATQw9hMpewxgyhpb1KhpF1Pc/OsEbl4fOFEpCdZsONEMxas+By/eW83OlweMMbkqeyJFPExmSRMH5oNqzn2ZETsrbgXklXq5SOmDFOaK/aJtJdPRUMnKpu7YDVLmFaSFdF7i1y4iGBEy7GGDjDGTwDx0mbh4kk83fXBzmp4fQxH6zvQ0uWBzWLCmAQ6mcQ6Dn9kvFGF7s0tOqa6AOCq00vw8U/m4NuTCuFjwAufleOCZZ9i1VfH0O7ywmqWMCwn9i9EEoG4ED6BVJey5mbZ2Eyl7DFPpMNKRZpr8pBMWWCHy/giXpG0u7Kl386voXK4lvuUhuemxlyIuS9mjciBI9mKujYnvj7SIE9kH1+YEZNXlImKWhEfp8crd/fWI9UlyEtPwoprpmHlTadhcGYyTjR14uG3dgIAhuemwWahz2osEBd/JWE4VSvVRRGf2EdEfI42dAw4DTyYjX5fzswI/D2CEYP4AbHN6UFFY0fEryMor/MLn0Hx0y/EajbhgvF8Yvt/dlZhO01kj0kCJe3KRnyEvweAIUaXnDs2D2vuOwe3zi6FcEGMLaTIZKwQF8InRaV5XSLiQwNKY5/8DDvS7RZ4fQxH6kIXH5E0LuyJ1WzCGH+Z666TLRG/jqC8jket4sHfE8zF/maG/9lZiW8qmgDQRPZYQ60mhm1+4ZNmtxjGb5lis+DhS8bjnbvPxvVnDMVd547Ue0lEiMSV8FHc40MDSuMGSZIClV0hprsqGjpwoqkTFpOE6UMj8/cIJvjTXbtORu/zERGfeBM+Z43MRXqSBdUtTnx9tBEAMJkiPjFFYGyFssJHz1L2gSgb7MCvLi9LmB4+8UBcCJ9k/7wupcvZKdUVX8gzu2pCK2kX1VwThzii7sYq+3wUifjEp/CxW8y4YFy+fDvFZo6rdF4iINp+NHcqnerSr6KLiD/iQvioluqiqq64YlSYEZ9A/57I01yCQMQnOuHT3OmWuzbHm/ABgIsmFsr/LxvsMExagwgNOeLTrmzEp0XHHj5E/BFXwqdT4ZEVDVTVFVeEW9IuGg7OLI3c2CwYW5ABSQJqWp2o9Q8YjQQR7cnPsMflTKDZo3Jl8ypNZI89ZI+PwlVdIuJjBGMzEfvEhfChqi4iFERJ++G69m6jEXrjRFMnKho6YTZJmDEseuGTareg1N/jY3dl5FGfeDU2C5KsZlzhb39/XlDai4gN1K7qolQXoQRxIXzUMDf7fEweWUEDSuODwVnJsFtMcHl8qGjs7HdbMZ+rbLBDsatMJXw+5bXC3xO/3peHLxmHjQ+dhzNHRJ9iJLRFeHwaVRM+lOoioieuhI+SEZ+mTjdErzma0xUfmE0SRgwKLd0lytjPUCDNJRivQGXX4bpA88J4xWo2xcUMskQkM5ULky63D10KFpuIVFcGRXwIBYgL4ZNsU76qS5SyZyRZqBtnHBFqZdeXCjQu7MmEIu5ZiSriE6cVXUR8kB7UZ0fJXj6U6iKUJC7O6GqkukRFV04aGZvjiVAqu6qau3CkvgMmCYr4ewTjC3nEp7y+He3O8I34jLGA8BlEwocwHpIkBXw+Cpa0tzpFOTuluojoiQvhI2YodSg4q4uMzfFJKJVdItozociBDAUPtIPS7chLt4MxYG9V+FGfmlYnOlxemE0SirNSFFsXQSiJQ4WSdor4EEoSH8JHhaquehI+ccmo/IDwYaz3gaGif48SZew9icbgLIaTFmclU/qVMCxqNDEkczOhJHFx9FQj1RXo4UPCJ54YmpMKi0lCh8uLk81dvW4jG5sVaFzYk2gaGZK/h4gFRKpLybEV1LmZUJK4Ej5KRnwo1RWfWM0mDPMLh57pLpfHh2Vr9uNwXTskCThNjYhPod/gHEEvn0APn/gtZSdiHzUGlVKqi1CSuBA+alR1UaorfhnpL2k/UB2o7Np5ohkLVnyOP358AABw45nD4EhWPqwuIj57q1rhHqCJYk/I2EzEAmJshZJNDIXwUdJzRyQucSGfU/weH5fHB6+PKTLfR57MTs0L445R+Wn4YBdwqLYNTo8XT398EM+uPwSvjyE71YZfXVaGSyYVDvxCEVCSnYI0uwVtTg8O17ZjTEHoE50ToYcPEftkycJHmYiP2+uTL2op4kMoQVx8ikRVFwB0uDyKGOACA0qpnD3eEJVdGw834NKnP8f+ap5CumRSIR5bMEHVFgYmk4RxhenYdKQRu042hyx8PF4fjtV3ACCPD2FsHAp3b27rClTrxuN8OkJ74iLVZbeYIPmDPEoZnMncHL+I7s3lde3YX92G3DQbnr12Gv50zTRN+jaJRobhGJyPN3bC42NIsppQkEFdjQnjIkd8FBpUKtJcyVYzrOa4OGUROhMX8lmSJKRYzWh3eRUxODMWmNNFHp/4Y2ReGpKtZnS6vVgwuQiLF0zQ9O8sGhmGU9Iu/D3DclJhUiCVSxBqkSWbm5WJ+LRQRRehMHHzSUq2WdDu8ipicG7p8sDt5T1eSPjEH0lWM165dSZcHp8qJesDETyzizEGSRpYyMj+HjI2EwbHoXA5O1V0EUoTN58kJUvaRZor1WZGktU8wNZELDKtJEu39x6VnwaLSUJLlwcnmjoxJIQuzIdrRSk7CR/C2GT5LxabO9whC/v+CPTwoYouQhlUT5guXboUkiRh0aJF8n2MMSxevBhFRUVITk7G3LlzsWvXrqjeR8kmhqKiK5squggVsFvMGJXPTc2h+nwCzQuphw9hbEQDQ5fXp8iFaJuTIj6EsqgqfDZt2oTnn38ekyZN6nb/k08+iWXLlmHFihXYtGkTCgoKcMEFF6C1tf+J2f2RJI+tiH5eF1V0EWoTrs+HujYTsUKKzQyb34SshMGZevgQSqOa8Glra8O1116LF154AVlZgbQCYwzLly/Hww8/jIULF6KsrAwvvfQSOjo6sGrVqojfT474KODxoYouQm3CGV3R4fKg0j9eg3r4EEZHkqSgQaXRG5xpXAWhNKoJn7vuuguXXHIJzj///G73l5eXo6qqCvPmzZPvs9vtmDNnDjZs2NDrazmdTrS0tHT76YmSHh/q2kyoTWBYafOA2x6p4/17MlOssn+CIIyMKGlvVjDiQ8KHUApVhM9rr72GzZs3Y+nSpac8VlVVBQDIz8/vdn9+fr78WE+WLl0Kh8Mh/xQXF5+yjTy2QkFzM0V8CLUQwudkc9eAV8WU5iJijcxk5ZoYttBkdkJhFBc+FRUVuPfee/HKK68gKanvRms9nf79uf8feughNDc3yz8VFRWnbCPGViiZ6qKID6EWGUlWlGTzaq6BBpYGhpOS8CFig0wFx1ZQqotQGsWFz+bNm1FTU4Pp06fDYrHAYrFg/fr1+OMf/wiLxSJHenpGd2pqak6JAgnsdjsyMjK6/fQk2aaguZmED6EBoRqcaUYXEWsoOahUpLrSaFwFoRCKC5/zzjsPO3bswLZt2+SfGTNm4Nprr8W2bdswfPhwFBQUYM2aNfJzXC4X1q9fj1mzZkX8vsmK9vGhAaWE+kwIamTYH1TKTsQage7NSkZ8KNVFKIPiEjo9PR1lZWXd7ktNTUVOTo58/6JFi7BkyRKMGjUKo0aNwpIlS5CSkoJrrrkm4veVU11KCB8qZyc0QDY4D5jqIo8PEVvIVV2KCB9Rzk4RH0IZdPkkPfDAA+js7MSdd96JxsZGzJw5E6tXr0Z6emiTqntDqYgPY0xOdZG5mVATMaz0UG07utzeXruEN7a75KvmYbkDd3gmCCOg5LyuVjI3EwqjifBZt25dt9uSJGHx4sVYvHixYu+RIqq6ojQ3d7i8cHp8AMjjQ6hLfoYd2ak2NLS7sK+qFZOLM0/ZRvh7ihxJ8mecIIyOkhPaydxMKI3qIyu0QqmRFaKiy24xya9JEGogSZLs89l8tLHXbeQ0Fw0nJWIIh0Ll7F4fQ7v/mE7Ch1CKuBE+So2sCE5zRTtcjyAGYtaIXADAsjX7sb/61JEtVMpOxCJZqf4GhlF6fMScLoBSXYRyxI3wUapzMw0oJbTkltmlmFmajTanB7e89PUpzQypoouIRUQDw6ZOPqE9UkSay24xwWaJm9MVoTNx80lSalYXDSgltMRqNuHZ66ajODsZxxo6cOcrW+D2+uTHD9dSDx8i9hB9fLw+hlZn5FF4MjYTahA3widZYY8PVXQRWpGdasP/3nAaUm1mfHG4Ho/9ezcAwOdjOFJPpexE7JFkNSPJ6p/Q3h55uotK2Qk1iBvhk6LQrC4aV0HowZiCdCy/aiokCXh541H8beNRVLZ0ocvtg8UkYUhWst5LJIiwkEvaOyM3OFNFF6EGcSR8/B4ftzeqnDKNqyD04oLx+bh/3hgAwOJ3duHVL48BAEpyUmAxx81XlUgQHMnRNzGUx1WQ8CEUJG6OpqKqy+tjcAV5JMKFUl2Entw5dwQum1IEj49hxdqDAMjfQ8QmSjQxlCM+dvL4EMoRN8InuOdONOkuivgQeiJJEp74n0mYPMQh30f+HiIWUWJCe4tsbqaID6EccSN8rGYTrGbedyeaknYaUEroTZLVjOeun4G8dF5ZODo/8lEuBKEXmSnRNzGkqi5CDeJKRidbzXB7PVGVtNOAUsIIFDiS8Pfbz8Qne2uwYEqR3sshiLDJUiDi0+YkczOhPHET8QGir+zqcnvl9uiU6iL0ZlhuKn5wdinsFhqdQsQegVSXEhEfEj6EcsSZ8Imue7MwNlvNEvWNIAiCiILMlED35kgJ9PGhVBehHHElfKKd1yWET1YKzekiCIKIhkxFytkp1UUoT1wJn2gntFNFF0EQhDJk+Y+jzVGkuoQ/KCOZIj6EcsSV8EmOOtVFFV0EQRBKoETEp6aVH5NFhSNBKEFcCZ9oB5XSgFKCIAhlEB6fli43vL7wu+k7PV40+/1BeelJiq6NSGziTPhEV9VFXZsJgiCUQYysYAxoicDgXOuP9tgsJmQkk8eHUI64Ej4Bc3N0woc8PgRBENFhs5iQZueCJZLKLpHmGpRmp2ITQlHiSvgEBpVGVtVF5maCIAjlCAwqDd/gXNPi9/dkkPWAUJa4FD7RprpyydxMEAQRNVmpkTcxrG3tAkDGZkJ54kr4JCskfMjcTBAEET2BCe2Re3zI2EwoTVwJnxTh8Ym4qot/0SjVRRAEET2OKEraZY8PRXwIhYkv4RNFVZfb60OLvz06VXURBEFEj4j4RNLEkHr4EGoRV8InyRb5yIpGf5rLbJLkqxSCIAgicsSg0sgiPn6PD5mbCYWJK+EjUl2RRHzq5TldVphMVDpJEAQRLdEMKpWrusjjQyhMfAmfKEZWUA8fgiAIZRFjK8Kt6vL6mHwxSqkuQmniSvgkRzGygnr4EARBKEugnD28iE9DuwteH4Mk0TGZUJ64Ej7RmJsb/BVdOVTKThAEoQiOZC5awm1gKPw9Oal2WMxxdZoiDEBcfaIo1UUQBGEcsvzm5uYwIz5U0UWoSVwJHzGrq9PthS/MacCU6iIIglAWYW5udXrg9vpCfl4tjasgVCSuhI+I+ABAlye8qI88mZ3GVRAEQSiCI9kKMV+0OYzKrto2ivgQ6hFXwifZGhA+4aa7KOJDEAShLGaThIyk8Cu7alq4x4e6NhNqEFfCx2SSkGTluxSuwZk8PgRBEMoTSRPDGprTRahIXAkfIKiyK8ySdjnVRVVdBEEQipEZwaBSMjcTahJ3wkeku8JJdXl9TC63pIgPQRCEcmTKg0rDSHXRuApCReJP+EQwr6uioQOMAXaLiYQPQRCEgoRb0s4YQy2luggViTvhIyarVzV3hfycAzVtAICReWkw05wugiAIxRCprlAjPq1OD7rcvPSdzM2EGsSd8Bmdnw4A2F/dFvJz9le3dnsuQRAEoQzC3BzqoFIxnDQ9ySL3ZiMIJYk/4VMghE9ryM85GBTxIQiCIJQj3EGlsr+Hoj2ESsSf8PGLl3CEz4Eavu0oEj4EQRCKkpUaXlUX+XsItYk/4eNPVx1v7ES7c2CDs8/H5IjPKEp1EQRBKIojObw+PkL4kL+HUIu4Ez5ZqTb5CyNMy/1xvLETXW4fbBYTSrJT1F4eQRBEQpHlNzc3h5zqoh4+hLrEnfABgDHC4Fw1cLpLpLlGDKKKLoIgCKUJt3OzGFdBPXwItYhL4TMqP3Sfj4gKkb+HIAhCeUQ5e6fbi64QOurTuApCbeJS+IiIz74QhI8QRyR8CIIglCcjySJH00OZ0E6pLkJt4lL4CJPygRB6+ZCxmSAIQj0kSQoyOA/s86HJ7ITaxKXwGe1PdVW1dPV7hdG9oosiPgRBEGogNzEcwOfT5faipYtX41Kqi1CLuBQ+6UlWFDn4l+ZAP+muE02d6HB5YTVLGEoVXQRBEKoQahNDUcpus5iQkWxRfV1EYhKXwgcIpK768/mIaM/w3DRYzHH7qyAIgtAVUdI+UMQn2N8jSVRlS6hD3J7txxQM7POROzZTmosgCEI1HCGWtNfSuApCA+JW+IgqrX399PIRomhUHhmbCYIg1GJIZjIA4HBt/wUnNdS1mdCAuBU+csSnpm/hs5+MzQRBEKpTNtgBANhxornf7WhOF6EFcSt8xKT1ujYX6tucpzzOGMNB6uFDEAShOhOHcOFzoKat3yaGNS3Uw4dQn7gVPik2izx7a38vPp/K5i60u7ywmCQMy03VenkEQRAJQ0FGEnLTbPD6GHZXtvS5XU0rjasg1Edx4bN06VKcdtppSE9PR15eHi6//HLs27ev2zaMMSxevBhFRUVITk7G3LlzsWvXLqWXIvfz6S3dJUZVlOamwkoVXQRBEKohSZKc7trZT7qLxlUQWqD4GX/9+vW46667sHHjRqxZswYejwfz5s1De3u7vM2TTz6JZcuWYcWKFdi0aRMKCgpwwQUXoLV14BET4TBalLT3YnAW/X3I30MQBKE+k4TP5/jAwofMzYSaKN4h6oMPPuh2e+XKlcjLy8PmzZtxzjnngDGG5cuX4+GHH8bChQsBAC+99BLy8/OxatUq3H777YqtZXQ/oyvEfSOpoosgCEJ1BjI4e31M9mOSx4dQE9VzPM3N/EOenZ0NACgvL0dVVRXmzZsnb2O32zFnzhxs2LCh19dwOp1oaWnp9hMKo4OaGDLGuj0m0l+jKeJDEAShOgMZnOvbnfAxwCQBOWkkfAj1UFX4MMZw33334eyzz0ZZWRkAoKqqCgCQn5/fbdv8/Hz5sZ4sXboUDodD/ikuLg7p/YcPSoVJ4hOBRZmkWBf18CEIgtCOgQzOoqIrJ80uT3MnCDVQVfjcfffd2L59O1599dVTHuvZjpwx1meL8oceegjNzc3yT0VFRUjvn2Q1yxVbwaMrqlucaHV6YDZJGJZLM7oIgiDURpIkTOzH4FzbSmkuQhtUEz733HMP3nnnHaxduxZDhgyR7y8oKACAU6I7NTU1p0SBBHa7HRkZGd1+QmW0P6ITXNIu0lxDc1Jgt5hDfi2CIAgicib2Y3AWpexkbCbURnHhwxjD3XffjTfffBOffPIJSktLuz1eWlqKgoICrFmzRr7P5XJh/fr1mDVrltLLwWh/B+f9QZVdIs01mtJcBEEQmtGfwZkiPoRWKF7Vddddd2HVqlX417/+hfT0dDmy43A4kJycDEmSsGjRIixZsgSjRo3CqFGjsGTJEqSkpOCaa65RejmyeXl/UC8fGk5KEAShPT0NzknWQMSdevgQWqG48Hn22WcBAHPnzu12/8qVK3HTTTcBAB544AF0dnbizjvvRGNjI2bOnInVq1cjPV35CMyYoJJ24SMKlLKT8CEIgtAKbnC2o67Nid2VLZhWkiU/Jo+roK7NhMooLnx6lo33hiRJWLx4MRYvXqz025/CsNxUWM0S2pwenGzuQpEjSe7aTBVdBEEQ2sENzhlYu68WO443dxc+YlwFpboIlYn7WQ1WswnDc/3prqpW1LY60dzphkni5e4EQRCEdkzsw+dDXZsJrYh74QMEvDz7q1vlaM/QnNRu+WWCIAhCfXqb2cUYCzI3k8eHUJeEED5jgjo4ixld5O8hCILQnklDMgFwg3Oni3dwbunywOnxAaCID6E+CSF8RgUZnAP+HhI+BEEQWpOfYUdumr1bB+dav78nI8lCkXhCdRJC+IiS9gM1rfKkdjHHiyAIgtAOYXAGAumuQEUXpbkI9UkI4TM0JxU2iwldbh+2VjQBoFQXQRCEXvQ0OMvGZhpOSmhAQggfs0nCyEFc6Hh9DJIEjBhEwocgCEIPJvp9PiLiIxubqYcPoQEJIXwAYExBILVVnJWCZBvlkQmCIPRARHz2V7ei0+WlHj6EpiSM8AkeTzGaRlUQBEHohjA4+xiwu7KFxlUQmpIwwmdMkJl5JHVsJgiC0I2eBmcaV0FoScIIn+AqLiplJwiC0Bfh89lxollOdZG5mdCChBE+gzOTkWbno8mC/T4EQRCE9siVXcebydxMaIriQ0qNiskk4TffKcOhmjZMKMrQezkEQRAJjRA+B2pa4fPPth5EHh9CAxJG+ADAZVMG670EgiAIAgGDc10bj/bYLSZkJCXUKYnQiYRJdREEQRDGQZIkTBrikG/nZdghSZKOKyISBRI+BEEQhC6ISe0AGZsJ7SDhQxAEQejCxCDhQz18CK0g4UMQBEHoQjfhQxVdhEaQ8CEIgiB0QRicARpXQWgHCR+CIAhCFyRJwoyhWQCAoTmpOq+GSBSodpAgCILQjcULJmB+WT7mTyjQeylEgkDChyAIgtCNAkcSvjN1iN7LIBIISnURBEEQBJEwkPAhCIIgCCJhIOFDEARBEETCQMKHIAiCIIiEgYQPQRAEQRAJAwkfgiAIgiASBipnJ4hEwdUBfPok4O4EzrgTyBqq94oIgiA0h4QPQSQCVTuBN24Gavfy25v+F5h8NTD7J0B2qb5rIwiC0BBKdRFEPMMY8NULwAvf4qInLR8oPQfweYCtLwNPTwfevhOoP6T3SgmCIDQhsSI+jAGtlYCrHXC28n9d7YCrjf9kDweGzQYkSe+VEkT0tNcD79wN7Huf3x41D7jsGSBtEFDxFbD+CeDgR8C2V4BvXgUmfheY+yD/HhAEQahBzV69VwCJMcb0XkS4tLS0wOFwoLm5GRkZGaE9qb0eWPVd4MTm/rcrmQVc8Eug+PToF0oQelH+KfDmbVzom23ABY8BM+84VdQf38wF0IEP+e0kB/D9D4D88dqvmSCI+MbjRMsfz4HjJ1+Fd/5WmMQQPu31wF8XANU7AckE2NMBW5r/J5X/WJP5ycLTxZ8z9tvAef8PGDRG3Z0hlMXnA1qOA/UHefrG5wEKJgGFk/jfPRH4bBnw8WMAGJA7GvifF/n+98fJrcC79wEntwDphcDNq4HMEk2WSxBEgrD6EbSs/SMcj7eS8AmXsIRPex3w0gKgZhf3N9z4LjBodO/bNp8A1j8ObP0bwHxcJE25Fpj7EOAYrPyOaEFLJbB5JVC3H0jJAVJygVTxM4jfzi4FLHa9VxoZFV8Be98LCJ2Gw4DX2cuGEpA7CiicAhRN8f87FbClaLtetdn/IbDqe/z/024ALnycC/tQ6GgAVl4M1O4BckYBP/gQSM1Rb61Ed3xewOOMv8+kHrg6AOZNnIsdgcfFj4Ft1UB7Lf+3rYb/tNcCOSOBmbfpk84+vB7462VocfpI+ERCyMInHNETTO0+fsW8911+25IEnHUvcM4DgDlGbFHHvwY2PgvsfptHPfojNQ+44v+A0tmaLE0RqncBH/8K2P+fUx8zWbmYyxkJQAIqtwEtJ07dzu4Azvgh/0nOVHnBGtBaDTx7JtBRD8z8IXDR4+G/RvMJ4MV5PGpWNA248d+APU35tRLcZ3j8ay7eK74Ejm/i9034DvdaUbQ5dNpqgYqNwNEvgGNfAJXfcOGTMZhHPQeN5b9P8W9Ktt4rVp7Kb4BXr+79WNcNCRh3KT+nDZmhydLQ2Qg8exbQcgItY6+G4+rnSPiES0jCp62Wp7dqdgNpBcBN7/Ir/nCo+ApY8yhwbAO/PfRs4IoXgfSC6HZALbxuYPe/uOA58XXg/pJZwJiLAGcLF4Pttfzk2F7LT5auVkAyA+cvBmbdY2xzd+MRYO1SYPvrABhfd9n/8C9w9gggZwTgKD5VoLbVcgF0citwchv3erVV8cfsGdz/csYPY/eA6PMBr/wPcOgTIL8MuOVjwJoU2WvV7gf+bz7Q2QCM+BZw9euAxabsehOV+kPAl3/mJ+fqXTyy3CsSN5vP+RmQO1LTJcYEXg+/6Nn/AXBsI4/4hkPhZGDuz4HR8419vAuV/auBf9wEuNu5hSNjMJCWx39S/f8mZwJ73wcOrgk8r2QWP+aPvhAwqVTkzRjwzx8Au94Esoej5er34MgbTMInXAYUPm21wEuX8pB9pKJHwBiw8w3g34u4QEgdxD0Tw+dEtQ+K4vXwviz/Xc7NrAA3tJZdAZxxB/+S94WrA3j3x8D21/jt8ZcBl/3JeCHitlrgs98Bm14EfG5+3/jLgW89Etnf1ucD9vwLWP8kF8cAYEsHZt4OnHlX7AmgL/4EfPhzHp28bT2QNza61zv+Nf8OuTv452jhC+odGBOFXW8D/7qbH0cEmSVA8czADxj/TIpos2QCJl0FzPkpVdsBQGsVsPklnr4XxzoAgATkjQdKzgBKzuT/2tN5ir92L4/ii5/mY4GnDZ7BjyHD58auAPr6/4D37ucRrtI5wJUv8yKFvqjeDXyxAtj+98CxNGcUMOcBLraV/j1s/zvw5q38IvXm1WjJGB1+cZLCxJ/wOUX0vKfMFVPdQeAfNwYM0nMfAmbfr//J4NiXwHs/Aap38Ntp+cCMm4EZ3+cqPxQY48Lpg4f4FyF3NHDlK6GlBbVg04vA6l/wqxkAGH4uN54Pnhb9a/t8wN5/85NN9U5+ny0NmPUj4Owfx0ako3I78L/nAV4XcMlTwGm3KPO6Bz4CXr2Sp0pn/hC4cKm6J4fWauDwOv7TVgVMvwkYtyB2T0gCjwtY8wse6QH4iXnmHVzoZBT2/pyT24B1jwdSuZIZmHotMO83QJI+JwvdYAw48jk/Ru19N5C6Tx0ETLqS96UqPh1Izgrt9drrgS+eBr58jgt7gEfzv/UIMPRMdfZBDXw+4OPFwH//wG9PuRb49vLQj1ktJ/nv4OuVgLOZ31dyJnDxb4GCicqssekYT3E5W3iEbe7PIqvKVpj4Ez4vLQDK1/PKlBvfVTZM7O4E3v8pb/wGACPOAxY+z43CWtNeD3z0aGAtSZlcDEy9PvKTdcVXwN9v4FdStjTg8md4BEgvfF4ueDb+id8umsrTccPnqvBePmDfe7y0u8ovIgsmAd/5M5A/Qfn3UwpXB/D8XKBuHzDmYuCqVcoKBXG1BnCP27k/V+71nW3A0Q1+sbM2EHkLpuRMfrIfMl2Z99SapmM8BSHaaJy1CPjWL0L3Ch7fDKxbGkhPDBoLXP1aYnTbrjvACxe+eTXQcRzgn4nTbuE+lWiKMtpqeAXk1y/yiwaAH9MveAwoKItu7Wrj7gLevgPY9Ra/fe7DwDk/jey72dUCfPU88NlTXAhKJv77PffnoYvJ3vB5eRDi6H+BIafxNhlmCwmfSOnzF1f5DfDcOYDJAvzwC/UiFttW8dJfTyeQXgT8z/8Cw85S57164vNxsfPRo9wwBgBTrwPO/6UyAqythudjj3zGb599HxdUWl91uzr4CVeE/L/1Cz5eQe11iNTm+z/lHhezjR8AZv0IMJnVfe9IePfHPNSdlg/8cIM6Ilyk0QBuvL3sT6FXivVGez2wbgmw5a+BEw4AQOJl98PP5X/njX/m3zGAp9vOfzS2Suz3fQC8dTvQ1cQvTL7zHDDmwshe68h/+feyrQpIzga+91fjFSP4vPzK3p4R2XfF5+Up1n3vcS9K/YHAY9ZUYPKVPJqttChpPg58+jt+XPV5+Hf+wseBGT8wZrSxvR547Rpu5jZZgctWAJOviv51m48Dqx8JiKmUXH6hOeXayDIbny/n5ylrKvDDz+VULQmfCOnzF/fWD4FvVvGD5BUvqruI6t08OiK+nNNu4OJDTW9IxVf8BHR8E7+dXwZcsgwomans+3g9PIS64Wl++4y7gPm/0e4g0FbLUywnNvOD0OXPAhOv0Oa9Ba3VwL/vDaQahpzOoz85I7RdR3/sfY8fAAHg+re4EVktvv4/4P0HeCo0bwJw1SvhRx08Ln5luf7JQGg9s4QLneFzuT8huHy++QTwya/5FT8YYLZzA/rs+/r3MOiN1wN88ivuuQN4ddx3/xL9UNiWk/zvfXIrv7i7+Hc8pa0lTRXAjn/wk2RHHT8Jt9fy/3c0gBccmPhJMy2fp9vTC/i/KX5Rzrxc5DAvv5BjXl6JtP9D/loCk5WLu7Hf5t9/tf/mDeU83S++82VXAJcuN5bfsbMJ+L8LuZXD7gCu+htP9SnJ4fXAfx4IRNkGT+fRpBHfCi3C5mwD9rwDvPMjfrxYsAKYdr38MAmfCOn1F9dWA/x+Ar+CvOVjbcr0nK1ciGz5K7+dksPD8pOvUk4kMMZTAZ89FYjC2NJ4aPP029Qtr/96JfDuIv7/WfcAF/xKffFTux945Qqg6SgPs161Chg6S9337AvGeHTvgwf5lawlmYfBT7tFf29XSyXw7CwelTrzbi5M1ebYRuD164H2Gh7B+O7K0MQWY8C+/wCrH+Y9RgDuIZi/FBh29sCfqcpvgA8fDnz+UwcB1/6Dpz6NhrsL+Of3A2NCZt7BvzdKecXcncC/7uJRSQA4/XZg/hJ1jwOM8d/9l8/x/eqzEk0B7A5g1AXA2IuBkedrL3AZ48bfNY9yQZYzikfXjNDJ3Ovmx8bD67iV4/q3oy9i6O+9vnyO+8yEGd+ewau/xl8GjDyPN/0VuLt4OnbnGzzSKSK1Y78NXPm3bt9xEj4R0usvbt0TPHw+eAZw68faLujoFzzlULuH3x42m0diokm1+Xz8IPPZU7ybLsCvgCZfxVMvGUXRrzsUNr0IvHcf//9Z9/Kollri58jn/Iq2qxnIKgWu/acxSnmbKoB/3ck7ewM8QvGd54D0fH3W43UDL3+Hn4wKJnKhr1UDypaTwOvX8WicZOKfh/5aIFTt5BcH5ev57dQ8njqdck146RDGeOny6l/wKGuSA7jhX8YSP10t/PN75DMenfrOn4Gyhcq/D2O8wvGTX/Pbw8/lIjQaP0ZvuNp524ivXujuvxo2m/tsUnP5xV5wM9QkB//+tlX5G+dV80qsthreQkOSeLRKMvOLB8nMPwe2NB71G3qWMQoKjm0E/vF9oPUkv+D59jL+mdULxngEestLPHX0g//0X62rFK1V3Dy9623+uxBYU3krgOFz+O9qz7vdqxWzSnmF2Fn3ntIHjIRPhJzyi/O4gOVl/Ev2Py9qnxYB+Bo2/okLME8nFyln/zj83jBdzVwxf/77gJCyJPMKl1l3A44hqiy/X756AXj/fv5/tTw/21YFQqNDTgeuflUf03hf+HzcBLn6F/zvm5rHje0jztV+Lf/5Ga8QsqYCt63VvtGdu4tXEm77G79ddgW/Cmyu4CKxuYKbepsrAj40s523CZh9X3SpA2cr8Lf/4Q3/jCR+2ut5H6WTW3lbhGte49EsNdnzbz6Pzd3BTzSX/I5HSaKl+TjvBbb1ZX48AgBrCr/oOv02IG9c9O8RC7TX8d/vIf+F9NTrgIt+q09n7Q1Pc/8NJH5sHHORtu/v8/HecLve5r3iWo6fuk3GYC70y/6Hd8bv4xxBwidCTvnFffM68NZtPPy3aAdgtuq3uMYj3Bh7YHXgPkcxV+fBP+kFPCdeuY2H8iu/4SWsjeWB59kzgNNv5aXEaYM03pEefPkcz/sCPN977sPKiB+vm6cxvnqO3x5/GY+mBIdRjUTNXp7KqNkNQOKG67kPadfRe+vfeKoD4CHkcZdq8749kVsgPDhwZ/Dxl/EUYdYwZd67m/jJ9IufKcq8diQ0nwBevjwwFua6N7QTY5XbeZSpuYLfHn0RT3tG4kWr3g1s+CP38Ii/adYwLnamXBsf3c3DxefjUfd1S3iKr3Ayb+jZVxsCNdjzLo+ygvH08Jl3avfevcEYcGILnwpwbCP/7pX9D79gDcECQMInQrr94tLTgRfO5Vda5z7CG33pDWP8auyTX/My496wOwIGz55kDuVm6dNuMdbB5otngA8f4v+f8yBw7kPRvV5bLS/1Pfp54DXn/Ex//8xAuDv5CX/zX/jt4jO4mV7taFzFJuAvF3MfmxK/fyU48l/go8X8pJBZzEV+Zon/X/9tNfrOGEX81B3koqe5gl/xXv+29v2vOpt4G4avnueCxWTlJ8fZ9w/8u2eMtxT47x+AAx8G7h82m3vHRl1gzGpGrTm8nl/wdNTzSt6rX9Xm83ZyK5+f5+7gFW2XPGXMSrMwIOETId1+cU17gP+bx0Pp9+02VnoE4KHiqh2BqE7lN/zKUBgEs4cHRYKm8H+N3DV4wwpuUgWAM+7kZeaRhH5PbgVeu46HTG3pwMLngLGXKLtWtdn5BvDOvTy3nZwFXPYMN2WqQUsl79fTVsUNg9972fgCUW26WrjZUy/xU7kd+NvCwPDH69/mYk8vavfxqiSRmknLB857FJh8NRdEXU089Sh+Wqt4BFEebyMB4xcAs+6N3b5JatJQDrx6Fa92sqbwNiZqHrOaTwAvfIt/50d8C7jmH7EzK7IfSPhESLdf3Ic/4n0HplwHXP4nvZcWGq52Xt3iKDZWRCdU/vtH3okWABwlfBjmmItDvxLZ9io36nmd/IRx1arYHcjYcJibICu38dszbua9L5SMcri7gL9cwk9Qg8YBt6wxVomtnnS18MjP8a+0FT8H1gD/vJlHbQsmAde9qX86GvCbwD/kkVlRQWdJAjxdfT/HbOddoc+821jtGoxIVzOPUh/6BIDEU7hqzDd0tvGy9eod/Dt/84fGbuEQBiR8IkT+xR3bg4z/m8XLDu/4XLk228TA7H2Pm2yFt2DUPOCiJ/qfJ9RaDXy+LNC6f/SF3CAc619oj4une0SH6YzBvKov0mZ1wTDG5ztt+xs/sd+2lmY29aSb+HFw75PSvU0EPi8v8f30SX67ZBY3MhvtM+xxcoPyp78FXG3+OyW+zpRsHqFMyuRtP067JfTxNgTv0/SfB3ixA8C75V+yTLlqtM5G3jbiyGe8Wu6Wj6PvAWUgSPhEiPyLe/tBZGx9hs9Z+f57ei8r8XC1c+Pff//Iq7HMdl7JdvYibk5uqeTtyo98zn+CO7Ge8wA3BcdTuubwOh7JajzCb09YyMVgNCcVYSqXTNw0q2aTwlgmOO1lsnAvxPSblH2P9jrgjVv4eA2AC4b5S7RrJRAJzlZeSp6cxUUP+XWUgTHuqfrgQW5bGDYb+O5L3RtwRkLdQd68tf4gr9q84V9A8WnKrNkgkPCJEPkXt7gEGaxJ3+oWgs/Uef9+fuIHePrLYuNf3m5IvN383Idiz88TKq4OYP3j3AvFvPyqev4S3gMknHC4183nZL1zD3+deb/h7QyIvnF38ujYzn/y22fcyZsHKuGLqPgK+PuNvJeJNQW49I/ApO9G/7pEbLN/NTc9u9r4KJHzF/MIUCQXdIfW8kHYXc1AxhAeSYzDLAYJnwiRf3EPpiMjbyhw7za6ktEbxrjX6sOf8yGnALjQmcivhoadzScfK91kzaic3MZFS9V2fnv4XO6hKDnzlIZe3Wir4R2zv/4/bmoEgElX8WZ4MV7NoQmM8blLa/3N/UZewCvuIk1FMcZTs6sf4QbhnFHAlS8nTi8bYmCqdwFv3ArU7OK3B8/gEcdwvGZfvcCtA8zLy8KveiVu048kfCKkm/D59q+Bs36k95IIgbMV2P0O9xGUnJE4Qqc3vB7e/n7d0oC51GTlk4qHz+FiaPB03nfq+Nc8rbXrLZ42BHhVzoybeerQyOkUI7LrbeCtO3izyUgnmld+wz0ye/7Nb09YCCz4IxnLiVPxenjqa+0SfwdjCTjtZuBbj/R/DPS6ueARfqFJVwGX/gGwJmmybD0g4RMh8i/ukTxk/HxvYp9cCeNTf4j3STm0Fmg+1v0xWxofP1K3P3DfkNOBmbcD4xYYo31/rHJyK/Dq1TwCmZzNp8qXntN/xK2jAdjxT2DrX3kbCoCL1fm/4Y38KOpG9EdLJY8OinRrSi5w3i+A3NFBg1mDhrRufNY/zkUCzn8UOGtR3H/GEl74PPPMM/jtb3+LyspKTJgwAcuXL8fs2bMHfJ78i3v9TmR8L0ZK2AmCMd6Z+/B67ocq/5QPGQW4Mbzsf4CZtxljBEO80FIJvHY1F0EAAAnIHcVL0EX/rIKJvB3B1r/xLrleJ9/UbOPewVn30N+ECI/yT4H37u+7gW0w1lR/TyCVeoAZjIQWPq+//jquv/56PPPMMzjrrLPw3HPP4X//93+xe/dulJSU9Ptc+Rd3aDMyhk/TaMUEoTA+H1C9k1e7lc4xXvPNeMHVwftO7X2/+6DFviiYCEy9gc/8M3IzUcLYeFzAxmeAb17lKS2ThXtRgwe0pg7isw8LyvRerWYktPCZOXMmpk2bhmeffVa+b9y4cbj88suxdOnSfp9rhF8cQRAxSFsN77gcPCOv6Sg3P0/8HjDtem2mXhNEgmKE87cu/a9dLhc2b96MBx98sNv98+bNw4YNG07Z3ul0wul0yrdbWlpUXyNBEHFIWh4w6nz+I+hq4d2NyU9FEAmBLt3j6urq4PV6kZ+f3+3+/Px8VFVVnbL90qVL4XA45J/iYh3n4RAEEV8kZZDoIYgEQte2uVIP9zpj7JT7AOChhx5Cc3Oz/FNRUaHVEgmCIAiCiCN0SXXl5ubCbDafEt2pqak5JQoEAHa7HXY79TEhCIIgCCI6dIn42Gw2TJ8+HWvWrOl2/5o1azBr1iw9lkQQBEEQRAKgS8QHAO677z5cf/31mDFjBs4880w8//zzOHbsGO644w69lkQQBEEQRJyjm/C58sorUV9fj8ceewyVlZUoKyvD+++/j6FDh+q1JIIgCIIg4pzYHllBfXwIgiAIImYwwvlb16ougiAIgiAILSHhQxAEQRBEwkDChyAIgiCIhIGED0EQBEEQCQMJH4IgCIIgEgYSPgRBEARBJAwkfAiCIAiCSBh0a2AYDaL1UEtLi84rIQiCIAgiVMR5W88WgjEpfOrr6wEAxcXFOq+EIAiCIIhwqa+vh8Ph0OW9Y1L4ZGdnAwCOHTum2y9OL0477TRs2rRJ72VoDu13YkH7nVjQficOzc3NKCkpkc/jehCTwsdk4tYkh8ORcCMrzGZzwu0zQPudaNB+Jxa034mHOI/r8t66vTMREXfddZfeS9AF2u/EgvY7saD9JrSEhpQSBEEQBKEJRjh/x2TEx26349FHH4Xdbtd7KQRBEARBhIgRzt8xGfEhCIIgCIKIhJiM+BAEQRAEQUQCCR+CIAiCIBIGEj4a8swzz6C0tBRJSUmYPn06PvvsM/mxxYsXY+zYsUhNTUVWVhbOP/98fPnllwO+5o4dOzBnzhwkJydj8ODBeOyxx07piLl+/XpMnz4dSUlJGD58OP785z8rvm/90d9+A8CePXuwYMECOBwOpKen44wzzsCxY8f6fc1Y3+/q6mrcdNNNKCoqQkpKCi688EIcOHBgwNc0+n5/+umnuPTSS1FUVARJkvD222/Lj7ndbvzsZz/DxIkTkZqaiqKiItxwww04efLkgK8by/sNADfddBMkSer2c8YZZwz4urG+321tbbj77rsxZMgQJCcnY9y4cXj22WcHfF0j7/fSpUtx2mmnIT09HXl5ebj88suxb9++btu8+eabmD9/PnJzcyFJErZt2xbSaxt5v+MKRmjCa6+9xqxWK3vhhRfY7t272b333stSU1PZ0aNHGWOMvfLKK2zNmjXs0KFDbOfOnezmm29mGRkZrKamps/XbG5uZvn5+eyqq65iO3bsYG+88QZLT09nv/vd7+RtDh8+zFJSUti9997Ldu/ezV544QVmtVrZP//5T9X3mbGB9/vgwYMsOzub/fSnP2Vbtmxhhw4dYu+++y6rrq7u8zVjfb99Ph8744wz2OzZs9lXX33F9u7dy2677TZWUlLC2tra+nzNWNjv999/nz388MPsjTfeYADYW2+9JT/W1NTEzj//fPb666+zvXv3si+++ILNnDmTTZ8+vd/XjPX9ZoyxG2+8kV144YWssrJS/qmvr+/3NeNhv2+55RY2YsQItnbtWlZeXs6ee+45Zjab2dtvv93naxp9v+fPn89WrlzJdu7cybZt28YuueSSU767f/3rX9kvf/lL9sILLzAAbOvWrQO+rtH3O57QRfj86U9/YsOGDWN2u51NmzaNffrpp/JjPp+PPfroo6ywsJAlJSWxOXPmsJ07dw74mtu3b2fnnHMOS0pKYkVFReyXv/wl8/l83bZZt24dmzZtGrPb7ay0tJQ9++yziu9bX5x++unsjjvu6Hbf2LFj2YMPPtjr9s3NzQwA++ijj/p8zWeeeYY5HA7W1dUl37d06VJWVFQk7/sDDzzAxo4d2+15t99+OzvjjDMi3ZWwGGi/r7zySnbdddeF9Zqxvt/79u1jALp9rj0eD8vOzmYvvPBCn68ZC/sdTG8nwp589dVXDIAshHsjHvb7xhtvZJdddllYrxMP+z1hwgT22GOPdbtv2rRp7JFHHunzdWJtv2tqahgAtn79+lMeKy8vD1n4xMp+x8P5W/NU1+uvv45Fixbh4YcfxtatWzF79mxcdNFFcmrjySefxLJly7BixQps2rQJBQUFuOCCC9Da2trna7a0tOCCCy5AUVERNm3ahKeffhq/+93vsGzZMnmb8vJyXHzxxZg9eza2bt2Kn//85/jRj36EN954Q/V9drlc2Lx5M+bNm9ft/nnz5mHDhg29bv/888/D4XBg8uTJ8v033XQT5s6dK9/+4osvMGfOnG5lgfPnz8fJkydx5MgReZue7zt//nx8/fXXcLvdCuxd3wy03z6fD++99x5Gjx6N+fPnIy8vDzNnzuw1TRBP++10OgEASUlJ8mNmsxk2mw2ff/65fF+s7XckNDc3Q5IkZGZmyvfF636vW7cOeXl5GD16NG699VbU1NR0ezwe9/vss8/GO++8gxMnToAxhrVr12L//v2YP3++vE2s73dzczMAhD2CIRb3O17O35oLn2XLluHmm2/GLbfcgnHjxmH58uUoLi7Gs88+C8YYli9fjocffhgLFy5EWVkZXnrpJXR0dGDVqlV9vuYrr7yCrq4u/OUvf0FZWRkWLlyIn//851i2bJmcH/3zn/+MkpISLF++HOPGjcMtt9yCH/zgB/jd736n+j7X1dXB6/UiPz+/2/35+fmoqqqSb7/77rtIS0tDUlISfv/732PNmjXIzc2VHy8sLERJSYl8u6qqqtfXFI/1t43H40FdXZ0yO9gHA+13TU0N2tra8Pjjj+PCCy/E6tWr8Z3vfAcLFy7E+vXr5e3jbb/Hjh2LoUOH4qGHHkJjYyNcLhcef/xxVFVVobKyUt4+1vY7XLq6uvDggw/immuu6dbILB73+6KLLsIrr7yCTz75BE899RQ2bdqEb33rW7IIBuJzv//4xz9i/PjxGDJkCGw2Gy688EI888wzOPvss+VtYnm/GWO47777cPbZZ6OsrCys58bifsfL+VvTWV3iSvjBBx/sdr+4Ei4vL0dVVVU3RWu32zFnzhxs2LABt99+OwCulI8cOYJ169YB6FspP/TQQzhy5AhKS0v7VMovvvgi3G43rFarSnsdQJKkbrcZY93uO/fcc7Ft2zbU1dXhhRdewPe+9z18+eWXyMvLA8BNdaG8Zs/7Q9lGTfrab5/PBwC47LLL8OMf/xgAMGXKFGzYsAF//vOfMWfOHADxt99WqxVvvPEGbr75ZmRnZ8NsNuP888/HRRdd1G37WN3vUHC73bjqqqvg8/nwzDPPdHssHvf7yiuvlP9fVlaGGTNmYOjQoXjvvfewcOFCAPG533/84x+xceNGvPPOOxg6dCg+/fRT3HnnnSgsLMT5558PILb3++6778b27du7RWpDJdb2O57O35pGfAa6EhaqdqDISKwp5dzcXJjN5m77AAA1NTXd1pSamoqRI0fijDPOwIsvvgiLxYIXX3yxz9ctKCjo9TWBwP73tY3FYkFOTk5U+zUQA+13bm4uLBYLxo8f3+3xcePG9VvVFev7DQDTp0/Htm3b0NTUhMrKSnzwwQeor69HaWlpn69r9P0OFbfbje9973soLy/HmjVrBmxbHy/7HUxhYSGGDh3abyVfrO93Z2enfOV+6aWXYtKkSbj77rtx5ZVX9nulHiv7fc899+Cdd97B2rVrMWTIkKhfz+j7HU/nb13K2QeKfAz0+NKlS/HXv/51wNfseb9eStlms2H69OlYs2ZNt/vXrFmDWbNm9fk8xli3UHhPzjzzTHz66adwuVzyfatXr0ZRURGGDRsmb9PzfVevXo0ZM2aoHuUaaL9tNhtOO+20U0pB9+/fj6FDh/b5urG+38E4HA4MGjQIBw4cwNdff43LLrusz9c1+n6HghA9Bw4cwEcffRTSwToe9rsn9fX1qKioQGFhYZ/bxPp+u91uuN3uU6Zwm81mOdrbG0bfb8YY7r77brz55pv45JNP+r1YCQej77cgLs7fEduiI8DpdDKz2czefPPNbvf/6Ec/Yueccw47dOgQA8C2bNnS7fEFCxawG264oc/Xvf7669mCBQu63bdlyxYGgB0+fJgxxtjs2bPZj370o27bvPnmm8xisTCXyxXNboWEKG9+8cUX2e7du9miRYtYamoqO3LkCGtra2MPPfQQ++KLL9iRI0fY5s2b2c0338zsdns3R/yDDz7Irr/+evl2U1MTy8/PZ1dffTXbsWMHe/PNN1lGRkav5Y8//vGP2e7du9mLL76oS1l3b/vNGP8bWK1W9vzzz7MDBw6wp59+mpnNZvbZZ5/F9X7//e9/Z2vXrmWHDh1ib7/9Nhs6dChbuHBht9eIxf1ubW1lW7duZVu3bmUA2LJly9jWrVvZ0aNHmdvtZgsWLGBDhgxh27Zt61ba7XQ643a/W1tb2U9+8hO2YcMGVl5eztauXcvOPPNMNnjwYNbS0hK3+80YY3PmzGETJkxga9euZYcPH2YrV65kSUlJ7JlnnonZ/f7hD3/IHA4HW7duXbfPcEdHh7xNfX0927p1K3vvvfcYAPbaa6+xrVu3ssrKypjd73g6f2tezn766aezH/7wh93uGzduHHvwwQeZz+djBQUF7IknnpAfczqdzOFwsD//+c99vuYzzzzDMjMzux08H3/88VPKAMeNG9fteXfccYfmZYBDhw5lNpuNTZs2TS5/7OzsZN/5zndYUVERs9lsrLCwkC1YsIB99dVX3Z5/4403sjlz5nS7b/v27Wz27NnMbrezgoICtnjx4l7LAKdOncpsNhsbNmyYpmX8jPW934IXX3yRjRw5kiUlJbHJkyef0uMjHvf7D3/4AxsyZAizWq2spKSEPfLII90+v4zF5n6vXbuWATjl58Ybb5RLe3v7Wbt2rfwa8bbfHR0dbN68eWzQoEHy3/vGG29kx44d6/Ya8bbfjDFWWVnJbrrpJlZUVMSSkpLYmDFj2FNPPdVtH2Jtv/v6DK9cuVLeZuXKlb1u8+ijj8rbxNp+MxY/52/Nhc9AV8KPP/44czgc7M0332Q7duxgV199NSssLIz5KyOCIAiCiGXi5fytWwPDvq6ERQOkgoICZrfb2TnnnMN27NjR7fmxqJQJgiAIItaJh/O3xFiPQSAEQRAEQRBxCg0pJQiCIAgiYSDhQxAEQRBEwkDChyAIgiCIhIGED0EQBEEQCQMJH4IgCIIgEgYSPgRBEARBJAyaCp9nnnkGpaWlSEpKwvTp0/HZZ5/Jj7355puYP38+cnNzIUkStm3bFtJrrlu3DpIkoampSZ1FEwRBEESC09f52+1242c/+xkmTpyI1NRUFBUV4YYbbsDJkycHfE29zt+aCZ/XX38dixYtwsMPP4ytW7di9uzZuOiii+Qp3O3t7TjrrLPw+OOPa7UkgiAIgiAGoL/zd0dHB7Zs2YJf/OIX2LJlC958803s378fCxYs0HvZfRNx68MwOf3009kdd9zR7b6xY8eyBx98sNt9Yp7P1q1bQ3pdMSumsbGRMcZYXV0du+qqq9jgwYNZcnIyKysrY6tWrer2nDlz5rB77rmH/fSnP2VZWVksPz+/2wwVgiAIgiA4oZ6/BV999RUDIA+r7Qu9zt+aRHxcLhc2b96MefPmdbt/3rx52LBhg6Lv1dXVhenTp+Pdd9/Fzp07cdttt+H666/Hl19+2W27l156Campqfjyyy/x5JNP4rHHHsOaNWsUXQtBEARBxDKRnL+bm5shSRIyMzPDei+tzt+aCJ+6ujp4vV7k5+d3uz8/Px9VVVWKvtfgwYNx//33Y8qUKRg+fDjuuecezJ8/H//4xz+6bTdp0iQ8+uijGDVqFG644QbMmDEDH3/8saJrIQiCIIhYJtzzd1dXFx588EFcc801yMjICOu9tDp/a2puliSp223G2Cn39cUdd9yBtLQ0+acvvF4vfvOb32DSpEnIyclBWloaVq9eLXuJBJMmTep2u7CwEDU1NSHuCUEQBEEkDqGcv91uN6666ir4fD4888wz8v1GO39bQt4yCnJzc2E2m09RhzU1NaeoyL547LHHcP/99w+43VNPPYXf//73WL58uewyX7RoEVwuV7ftrFZrt9uSJMHn84W0FoIgCIJIBEI9f7vdbnzve99DeXk5Pvnkk27RHqOdvzURPjabDdOnT8eaNWvwne98R75/zZo1uOyyy0J6jby8POTl5Q243WeffYbLLrsM1113HQDA5/PhwIEDGDduXGSLJwiCIIgEJZTztxA9Bw4cwNq1a5GTk9PtNYx2/tZE+ADAfffdh+uvvx4zZszAmWeeieeffx7Hjh3DHXfcAQBoaGjAsWPH5Nr/ffv2AQAKCgpQUFAQ8vuMHDkSb7zxBjZs2ICsrCwsW7YMVVVVJHwIgiAIIgL6O397PB5cccUV2LJlC9599114vV45OpSdnQ2bzRby+2h1/tZM+Fx55ZWor6/HY489hsrKSpSVleH999/H0KFDAQDvvPMOvv/978vbX3XVVQCARx99FIsXL+7zdUV4y2Lhu/KLX/wC5eXlmD9/PlJSUnDbbbfh8ssvR3Nzs0p7RhAEQRDxS3/n7yNHjuCdd94BAEyZMqXb89auXYu5c+f2+bp6nb8lxhhT9BU15rXXXsMtt9yCtrY2vZdCEARBEESI6HX+1iziozROpxOHDh3CihUrcP755+u9HIIgCIIgQkDv83fMDin9z3/+g5kzZyI1NRV//OMf9V4OQRAEQRAhoPf5O+ZTXQRBEARBEKESsxEfgiAIgiCIcCHhQxAEQRBEwqCb8Fm6dClOO+00pKenIy8vD5dffrncu0fAGMPixYtRVFSE5ORkzJ07F7t27eq2zdy5cyFJUrcfUQovaGxsxPXXXw+HwwGHw4Hrr78eTU1Nau8iQRAEQRAGQzfhs379etx1113YuHEj1qxZA4/Hg3nz5qG9vV3e5sknn8SyZcuwYsUKbNq0CQUFBbjgggvQ2tra7bVuvfVWVFZWyj/PPfdct8evueYabNu2DR988AE++OADbNu2Dddff70m+0kQBEEQhHEwjLm5trYWeXl5WL9+Pc455xwwxlBUVIRFixbhZz/7GQBeApefn48nnngCt99+OwAe8ZkyZQqWL1/e6+vu2bMH48ePx8aNGzFz5kwAwMaNG3HmmWdi7969GDNmjCb7RxAEQRCE/hjG4yM6M2ZnZwMAysvLUVVVhXnz5snb2O12zJkzBxs2bOj23FdeeQW5ubmYMGEC7r///m4RoS+++AIOh0MWPQBwxhlnwOFwnPI6BEEQBEHEN4ZoYMgYw3333Yezzz4bZWVlACDP+ug5vT0/Px9Hjx6Vb1977bUoLS1FQUEBdu7ciYceegjffPMN1qxZI79Ob8PR8vLyTpk2SxAEQRBEfGMI4XP33Xdj+/bt+Pzzz095TJKkbrcZY93uu/XWW+X/l5WVYdSoUZgxYwa2bNmCadOm9foavb0OQRAEQRDxj+6prnvuuQfvvPMO1q5diyFDhsj3i4nsPaMyNTU1p0SBgpk2bRqsVisOHDggv051dfUp29XW1vb7OgRBEARBxB+6CR/GGO6++268+eab+OSTT1BaWtrtcZG+EikrAHC5XFi/fj1mzZrV5+vu2rULbrcbhYWFAIAzzzwTzc3N+Oqrr+RtvvzySzQ3N/f7OgRBEARBxB+6VXXdeeedWLVqFf71r391q6xyOBxITk4GADzxxBNYunQpVq5ciVGjRmHJkiVYt24d9u3bh/T0dBw6dAivvPIKLr74YuTm5mL37t34yU9+guTkZGzatAlmsxkAcNFFF+HkyZNymfttt92GoUOH4t///rf2O04QBEEQhG7oJnz68tesXLkSN910EwAeFfrlL3+J5557Do2NjZg5cyb+9Kc/yQboiooKXHfdddi5cyfa2tpQXFyMSy65BI8++qhcHQYADQ0N+NGPfoR33nkHALBgwQKsWLECmZmZqu4jQRAEQRDGwjB9fAiCIAiCINRGd3MzQRAEQRCEVpDwIQiCIAgiYSDhQxAEQRBEwkDChyAIgiCIhIGED0EQBEEQCQMJH4IgCIIgEgYSPgRBEARBJAwkfAiCIAiCSBhI+BAEQRAEkTCQ8CEIgiAIImEg4UMQBEEQRMLw/wEcLKvBE98esAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# interpolate 함수를 통해 값들을 선으로 이어 생성된 값으로 채움\n",
    "# method 매개변수로 고차원 또는 비선형 함수를 사용시 좀더 매끄러운 값으로 채움\n",
    "df_ups.interpolate(method='cubic').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.642970Z",
     "start_time": "2022-08-07T14:23:20.614970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "      <th>price</th>\n",
       "      <th>volumn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:00:00</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:20:00</th>\n",
       "      <td>59.666667</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>26.642764</td>\n",
       "      <td>5.818205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 00:40:00</th>\n",
       "      <td>43.333333</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>14.358655</td>\n",
       "      <td>7.899008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:00:00</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 01:20:00</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>52.419122</td>\n",
       "      <td>6.878769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 22:40:00</th>\n",
       "      <td>69.333333</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>70.362148</td>\n",
       "      <td>6.241869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:00:00</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:20:00</th>\n",
       "      <td>61.666667</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>49.304519</td>\n",
       "      <td>4.313686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-01 23:40:00</th>\n",
       "      <td>66.333333</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>52.297315</td>\n",
       "      <td>5.030997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050-01-02 00:00:00</th>\n",
       "      <td>71.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         price    volumn      price    volumn\n",
       "2050-01-01 00:00:00  76.000000  1.000000  76.000000  1.000000\n",
       "2050-01-01 00:20:00  59.666667  3.333333  26.642764  5.818205\n",
       "2050-01-01 00:40:00  43.333333  5.666667  14.358655  7.899008\n",
       "2050-01-01 01:00:00  27.000000  8.000000  27.000000  8.000000\n",
       "2050-01-01 01:20:00  49.000000  6.666667  52.419122  6.878769\n",
       "...                        ...       ...        ...       ...\n",
       "2050-01-01 22:40:00  69.333333  5.666667  70.362148  6.241869\n",
       "2050-01-01 23:00:00  57.000000  5.000000  57.000000  5.000000\n",
       "2050-01-01 23:20:00  61.666667  6.000000  49.304519  4.313686\n",
       "2050-01-01 23:40:00  66.333333  7.000000  52.297315  5.030997\n",
       "2050-01-02 00:00:00  71.000000  8.000000  71.000000  8.000000\n",
       "\n",
       "[73 rows x 4 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# method에 따른 결측치 채운 값의 차이 확인\n",
    "pd.concat([df_ups.interpolate(method='linear'), \n",
    "           df_ups.interpolate(method='cubic')], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **다양한 시간변수 추출(Time Feature Engineering)**\n",
    "\n",
    "> **\"데이터가 `생성된 시간정보`에서 필요에 따라 연/월/일/시/분/초/요일 등의 `시간패턴 추정을 위한 변수 생성` 필요\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.777977Z",
     "start_time": "2022-08-07T14:23:20.644972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>index</th>\n",
       "      <th>Area Abbreviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2100-01-01</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2100-01-02</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2100-01-03</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2100-01-04</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2100-01-05</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21472</th>\n",
       "      <td>2158-10-16</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21473</th>\n",
       "      <td>2158-10-17</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21474</th>\n",
       "      <td>2158-10-18</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21475</th>\n",
       "      <td>2158-10-19</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21476</th>\n",
       "      <td>2158-10-20</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21477 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index Area Abbreviation\n",
       "0     2100-01-01                AF\n",
       "1     2100-01-02                AF\n",
       "2     2100-01-03                AF\n",
       "3     2100-01-04                AF\n",
       "4     2100-01-05                AF\n",
       "...          ...               ...\n",
       "21472 2158-10-16                ZW\n",
       "21473 2158-10-17                ZW\n",
       "21474 2158-10-18                ZW\n",
       "21475 2158-10-19                ZW\n",
       "21476 2158-10-20                ZW\n",
       "\n",
       "[21477 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>index</th>\n",
       "      <th>Area Abbreviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2100-01-01</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2100-01-02</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2100-01-03</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2100-01-04</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2100-01-05</td>\n",
       "      <td>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21472</th>\n",
       "      <td>2158-10-16</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21473</th>\n",
       "      <td>2158-10-17</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21474</th>\n",
       "      <td>2158-10-18</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21475</th>\n",
       "      <td>2158-10-19</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21476</th>\n",
       "      <td>2158-10-20</td>\n",
       "      <td>ZW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21477 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            index Area Abbreviation\n",
       "0      2100-01-01                AF\n",
       "1      2100-01-02                AF\n",
       "2      2100-01-03                AF\n",
       "3      2100-01-04                AF\n",
       "4      2100-01-05                AF\n",
       "...           ...               ...\n",
       "21472  2158-10-16                ZW\n",
       "21473  2158-10-17                ZW\n",
       "21474  2158-10-18                ZW\n",
       "21475  2158-10-19                ZW\n",
       "21476  2158-10-20                ZW\n",
       "\n",
       "[21477 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 시간 인덱스를 변수로 초기화\n",
    "df_test = df.iloc[:,:1].copy()\n",
    "df_test.index = pd.date_range('2100-01-01', periods=df.shape[0])\n",
    "df_test.reset_index(inplace=True)\n",
    "df_test2 = df.iloc[:,:1].copy()\n",
    "df_test2.index = date_range('2100-01-01', '2158-10-20')\n",
    "df_test2.reset_index(inplace=True)\n",
    "display(df_test, df_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.822972Z",
     "start_time": "2022-08-07T14:23:20.779972Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21477 entries, 0 to 21476\n",
      "Data columns (total 2 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   index              21477 non-null  datetime64[ns]\n",
      " 1   Area Abbreviation  21356 non-null  object        \n",
      "dtypes: datetime64[ns](1), object(1)\n",
      "memory usage: 335.7+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21477 entries, 0 to 21476\n",
      "Data columns (total 2 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   index              21477 non-null  object\n",
      " 1   Area Abbreviation  21356 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 335.7+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 인덱스 변수의 형태 차이 확인\n",
    "df_test.info(), df_test2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.852971Z",
     "start_time": "2022-08-07T14:23:20.824972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Area Abbreviation</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2100-01-02</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>2100-01-03</td>\n",
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       "      <th>21472</th>\n",
       "      <td>2158-10-16</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21473</th>\n",
       "      <td>2158-10-17</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21474</th>\n",
       "      <td>2158-10-18</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21475</th>\n",
       "      <td>2158-10-19</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21476</th>\n",
       "      <td>2158-10-20</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21477 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index Area Abbreviation  Year  Month  Day\n",
       "0     2100-01-01                AF  2100      1    1\n",
       "1     2100-01-02                AF  2100      1    2\n",
       "2     2100-01-03                AF  2100      1    3\n",
       "3     2100-01-04                AF  2100      1    4\n",
       "4     2100-01-05                AF  2100      1    5\n",
       "...          ...               ...   ...    ...  ...\n",
       "21472 2158-10-16                ZW  2158     10   16\n",
       "21473 2158-10-17                ZW  2158     10   17\n",
       "21474 2158-10-18                ZW  2158     10   18\n",
       "21475 2158-10-19                ZW  2158     10   19\n",
       "21476 2158-10-20                ZW  2158     10   20\n",
       "\n",
       "[21477 rows x 5 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 년/월/일 추출 및 변수 생성\n",
    "if 'Year' not in df_test.columns:\n",
    "    df_test['Year'] = df_test['index'].dt.year\n",
    "    df_test['Month'] = df_test['index'].dt.month\n",
    "    df_test['Day'] = df_test['index'].dt.day\n",
    "    \n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.883973Z",
     "start_time": "2022-08-07T14:23:20.854973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2100-01-04</td>\n",
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       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2100-01-05</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>21472</th>\n",
       "      <td>2158-10-16</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21473</th>\n",
       "      <td>2158-10-17</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21474</th>\n",
       "      <td>2158-10-18</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21475</th>\n",
       "      <td>2158-10-19</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21476</th>\n",
       "      <td>2158-10-20</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21477 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index Area Abbreviation  Year  Month  Day  Hour  Minute  Second\n",
       "0     2100-01-01                AF  2100      1    1     0       0       0\n",
       "1     2100-01-02                AF  2100      1    2     0       0       0\n",
       "2     2100-01-03                AF  2100      1    3     0       0       0\n",
       "3     2100-01-04                AF  2100      1    4     0       0       0\n",
       "4     2100-01-05                AF  2100      1    5     0       0       0\n",
       "...          ...               ...   ...    ...  ...   ...     ...     ...\n",
       "21472 2158-10-16                ZW  2158     10   16     0       0       0\n",
       "21473 2158-10-17                ZW  2158     10   17     0       0       0\n",
       "21474 2158-10-18                ZW  2158     10   18     0       0       0\n",
       "21475 2158-10-19                ZW  2158     10   19     0       0       0\n",
       "21476 2158-10-20                ZW  2158     10   20     0       0       0\n",
       "\n",
       "[21477 rows x 8 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시/분/초 추출 및 변수 생성\n",
    "if 'Hour' not in df_test.columns:\n",
    "    df_test['Hour'] = df_test['index'].dt.hour\n",
    "    df_test['Minute'] = df_test['index'].dt.minute\n",
    "    df_test['Second'] = df_test['index'].dt.second\n",
    "    \n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.928970Z",
     "start_time": "2022-08-07T14:23:20.887973Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>index</th>\n",
       "      <th>Area Abbreviation</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "      <th>Second</th>\n",
       "      <th>Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2100-01-01</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2100-01-02</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2100-01-03</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2100-01-04</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2100-01-05</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21472</th>\n",
       "      <td>2158-10-16</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21473</th>\n",
       "      <td>2158-10-17</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21474</th>\n",
       "      <td>2158-10-18</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21475</th>\n",
       "      <td>2158-10-19</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21476</th>\n",
       "      <td>2158-10-20</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21477 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index Area Abbreviation  Year  Month  Day  Hour  Minute  Second  \\\n",
       "0     2100-01-01                AF  2100      1    1     0       0       0   \n",
       "1     2100-01-02                AF  2100      1    2     0       0       0   \n",
       "2     2100-01-03                AF  2100      1    3     0       0       0   \n",
       "3     2100-01-04                AF  2100      1    4     0       0       0   \n",
       "4     2100-01-05                AF  2100      1    5     0       0       0   \n",
       "...          ...               ...   ...    ...  ...   ...     ...     ...   \n",
       "21472 2158-10-16                ZW  2158     10   16     0       0       0   \n",
       "21473 2158-10-17                ZW  2158     10   17     0       0       0   \n",
       "21474 2158-10-18                ZW  2158     10   18     0       0       0   \n",
       "21475 2158-10-19                ZW  2158     10   19     0       0       0   \n",
       "21476 2158-10-20                ZW  2158     10   20     0       0       0   \n",
       "\n",
       "       Week  \n",
       "0        53  \n",
       "1        53  \n",
       "2        53  \n",
       "3         1  \n",
       "4         1  \n",
       "...     ...  \n",
       "21472    42  \n",
       "21473    42  \n",
       "21474    42  \n",
       "21475    42  \n",
       "21476    42  \n",
       "\n",
       "[21477 rows x 9 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 몇번째 주 추출 및 변수 생성\n",
    "# dt.week 대신 dt.isocalendar().week 로 버전 변경 예정\n",
    "if 'Week' not in df_test.columns:\n",
    "    df_test['Week'] = df_test['index'].dt.isocalendar().week\n",
    "    \n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.958973Z",
     "start_time": "2022-08-07T14:23:20.930973Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>index</th>\n",
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       "      <td>2100-01-05</td>\n",
       "      <td>AF</td>\n",
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       "      <td>2158-10-16</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21473</th>\n",
       "      <td>2158-10-17</td>\n",
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       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
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       "      <td>42</td>\n",
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       "      <td>2158</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>21476</th>\n",
       "      <td>2158-10-20</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21477 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index Area Abbreviation  Year  Month  Day  Hour  Minute  Second  \\\n",
       "0     2100-01-01                AF  2100      1    1     0       0       0   \n",
       "1     2100-01-02                AF  2100      1    2     0       0       0   \n",
       "2     2100-01-03                AF  2100      1    3     0       0       0   \n",
       "3     2100-01-04                AF  2100      1    4     0       0       0   \n",
       "4     2100-01-05                AF  2100      1    5     0       0       0   \n",
       "...          ...               ...   ...    ...  ...   ...     ...     ...   \n",
       "21472 2158-10-16                ZW  2158     10   16     0       0       0   \n",
       "21473 2158-10-17                ZW  2158     10   17     0       0       0   \n",
       "21474 2158-10-18                ZW  2158     10   18     0       0       0   \n",
       "21475 2158-10-19                ZW  2158     10   19     0       0       0   \n",
       "21476 2158-10-20                ZW  2158     10   20     0       0       0   \n",
       "\n",
       "       Week  WeekDay  \n",
       "0        53        4  \n",
       "1        53        5  \n",
       "2        53        6  \n",
       "3         1        0  \n",
       "4         1        1  \n",
       "...     ...      ...  \n",
       "21472    42        0  \n",
       "21473    42        1  \n",
       "21474    42        2  \n",
       "21475    42        3  \n",
       "21476    42        4  \n",
       "\n",
       "[21477 rows x 10 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 요일 추출 및 변수 생성\n",
    "# 월요일이 0, 일요일이 6\n",
    "if 'WeekDay' not in df_test.columns:\n",
    "    df_test['WeekDay'] = df_test['index'].dt.weekday\n",
    "    \n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:20.988971Z",
     "start_time": "2022-08-07T14:23:20.960973Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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       "      <td>19</td>\n",
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       "      <td>3</td>\n",
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       "<p>21477 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index Area Abbreviation  Year  Month  Day  Week  WeekDay\n",
       "0     2100-01-01                AF  2100      1    1    53        4\n",
       "1     2100-01-02                AF  2100      1    2    53        5\n",
       "2     2100-01-03                AF  2100      1    3    53        6\n",
       "3     2100-01-04                AF  2100      1    4     1        0\n",
       "4     2100-01-05                AF  2100      1    5     1        1\n",
       "...          ...               ...   ...    ...  ...   ...      ...\n",
       "21472 2158-10-16                ZW  2158     10   16    42        0\n",
       "21473 2158-10-17                ZW  2158     10   17    42        1\n",
       "21474 2158-10-18                ZW  2158     10   18    42        2\n",
       "21475 2158-10-19                ZW  2158     10   19    42        3\n",
       "21476 2158-10-20                ZW  2158     10   20    42        4\n",
       "\n",
       "[21477 rows x 7 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시/분/초 변수 삭제\n",
    "if 'Hour' in df_test.columns:\n",
    "    del df_test['Hour']\n",
    "    del df_test['Minute']\n",
    "    del df_test['Second']\n",
    "    \n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:21.003979Z",
     "start_time": "2022-08-07T14:23:20.991973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2023, 1, 8, 0, 2, 35, 727068)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간 차이 계산을 위한 현재 시간 정보 확인\n",
    "dt.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:21.018977Z",
     "start_time": "2022-08-07T14:23:21.006972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2023, 1, 8, 0, 0)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간 차이 계산을 위한 현재 시간 정보에서 필요 정보 추출\n",
    "dt.datetime(dt.datetime.now().year, dt.datetime.now().month, dt.datetime.now().day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:21.033973Z",
     "start_time": "2022-08-07T14:23:21.021972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       28117 days\n",
       "1       28118 days\n",
       "2       28119 days\n",
       "3       28120 days\n",
       "4       28121 days\n",
       "           ...    \n",
       "21472   49589 days\n",
       "21473   49590 days\n",
       "21474   49591 days\n",
       "21475   49592 days\n",
       "21476   49593 days\n",
       "Name: index, Length: 21477, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간 차이 계산\n",
    "df_test['index'] - dt.datetime(dt.datetime.now().year, dt.datetime.now().month, dt.datetime.now().day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:21.049971Z",
     "start_time": "2022-08-07T14:23:21.036981Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 문자형 시간정보는 연산 불가하며 datetime 형식에서만 가능\n",
    "# df_test2['index'] - dt.datetime(dt.datetime.now().year, dt.datetime.now().month, dt.datetime.now().day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:21.079971Z",
     "start_time": "2022-08-07T14:23:21.052974Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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></th>\n",
       "      <th>index</th>\n",
       "      <th>Area Abbreviation</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Week</th>\n",
       "      <th>WeekDay</th>\n",
       "      <th>Time_Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2100-01-01</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>28117 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2100-01-02</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>5</td>\n",
       "      <td>28118 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2100-01-03</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>28119 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2100-01-04</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28120 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2100-01-05</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28121 days</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>21472</th>\n",
       "      <td>2158-10-16</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>49589 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21473</th>\n",
       "      <td>2158-10-17</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>49590 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21474</th>\n",
       "      <td>2158-10-18</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>49591 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21475</th>\n",
       "      <td>2158-10-19</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>49592 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21476</th>\n",
       "      <td>2158-10-20</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>49593 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21477 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index Area Abbreviation  Year  Month  Day  Week  WeekDay  Time_Diff\n",
       "0     2100-01-01                AF  2100      1    1    53        4 28117 days\n",
       "1     2100-01-02                AF  2100      1    2    53        5 28118 days\n",
       "2     2100-01-03                AF  2100      1    3    53        6 28119 days\n",
       "3     2100-01-04                AF  2100      1    4     1        0 28120 days\n",
       "4     2100-01-05                AF  2100      1    5     1        1 28121 days\n",
       "...          ...               ...   ...    ...  ...   ...      ...        ...\n",
       "21472 2158-10-16                ZW  2158     10   16    42        0 49589 days\n",
       "21473 2158-10-17                ZW  2158     10   17    42        1 49590 days\n",
       "21474 2158-10-18                ZW  2158     10   18    42        2 49591 days\n",
       "21475 2158-10-19                ZW  2158     10   19    42        3 49592 days\n",
       "21476 2158-10-20                ZW  2158     10   20    42        4 49593 days\n",
       "\n",
       "[21477 rows x 8 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간 차이 변수 생성\n",
    "if 'Time_Diff' not in df_test.columns:\n",
    "    df_test['Time_Diff'] = df_test['index'] - dt.datetime(dt.datetime.now().year, dt.datetime.now().month, dt.datetime.now().day)\n",
    "    \n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:21.094970Z",
     "start_time": "2022-08-07T14:23:21.081970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=70)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# timedelta 함수로 특정 시간 간격 생성 가능\n",
    "# 주/일/시/분/초/마이크로초/밀리초 생성 가능\n",
    "dt.timedelta(weeks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:21.109969Z",
     "start_time": "2022-08-07T14:23:21.096971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2023, 3, 19, 0, 2, 35, 820534)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 특정 시간과 시간정보 연산 가능\n",
    "dt.datetime.now() + dt.timedelta(weeks=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-07T14:23:21.140970Z",
     "start_time": "2022-08-07T14:23:21.111973Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Area Abbreviation</th>\n",
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       "      <td>2100-01-01</td>\n",
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       "      <td>117 days</td>\n",
       "    </tr>\n",
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       "      <td>2100-01-02</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2100-01-03</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>119 days</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2100-01-04</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2100-01-05</td>\n",
       "      <td>AF</td>\n",
       "      <td>2100</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>121 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>21472</th>\n",
       "      <td>2158-10-16</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>21589 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21473</th>\n",
       "      <td>2158-10-17</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>21590 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21474</th>\n",
       "      <td>2158-10-18</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>21591 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21475</th>\n",
       "      <td>2158-10-19</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>21592 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21476</th>\n",
       "      <td>2158-10-20</td>\n",
       "      <td>ZW</td>\n",
       "      <td>2158</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>21593 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21477 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index Area Abbreviation  Year  Month  Day  Week  WeekDay  Time_Diff\n",
       "0     2100-01-01                AF  2100      1    1    53        4   117 days\n",
       "1     2100-01-02                AF  2100      1    2    53        5   118 days\n",
       "2     2100-01-03                AF  2100      1    3    53        6   119 days\n",
       "3     2100-01-04                AF  2100      1    4     1        0   120 days\n",
       "4     2100-01-05                AF  2100      1    5     1        1   121 days\n",
       "...          ...               ...   ...    ...  ...   ...      ...        ...\n",
       "21472 2158-10-16                ZW  2158     10   16    42        0 21589 days\n",
       "21473 2158-10-17                ZW  2158     10   17    42        1 21590 days\n",
       "21474 2158-10-18                ZW  2158     10   18    42        2 21591 days\n",
       "21475 2158-10-19                ZW  2158     10   19    42        3 21592 days\n",
       "21476 2158-10-20                ZW  2158     10   20    42        4 21593 days\n",
       "\n",
       "[21477 rows x 8 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Time_Diff에서 28000일 제외하기\n",
    "if 'Time_Diff' in df_test.columns:\n",
    "    df_test['Time_Diff'] = df_test['Time_Diff'] - dt.timedelta(days=28000)\n",
    "    \n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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   "cols": {
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    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}