Data_preprocessing.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/heejjj/Sleeping_Project/blob/main/Data_preprocessing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# 회의록\n",
        "2. 데이터 전처리\n",
        "- HRV 데이터 활용 예정\n",
        "   => perse 카운트(개수 높으면 awake, 낮으면 deep)\n",
        "- 수면주기 일정하지 않음. 비정상성 데이터를 차분 or 미분 사용하여 정상성 데이터로 변환하는 방법 고민해볼 것\n",
        "- 최종 예측값만 맞으면 됨, 마지막 아웃풋 core(2)인지 아닌지만 중요. 즉, binary 분류 문제\n",
        "- 시계열 간격 맞추고 돌려볼 것\n",
        "- 방법론을 깊게 고민하기보다는 하나에 포커스 맞춰서 빨리 시도해보고 안되면 왜 안되는지 그때 판단할 것\n",
        "- 시점을 맞춰놓고 긴 것 기준으로 짧은 것들 매꾸기(패딩 or 보간 활용)\n",
        "- 중간지점 설정: 최빈값>평균>중위수 순으로 시도해볼 것 / 1Q, 3Q 등 이후에 추가해보기\n",
        "- 수면 시작시간/끝시간 각각 평균으로 기간 잡기, 편차 +-10까지는 인정\n",
        "- kaggle의 apple watch 데이터도 가져와 학습시키기\n",
        "\n",
        "\n",
        "<<방향성>>\n",
        "1. 출발점, 끝점 정리 (이상치 전처리 필수!)\n",
        "   -> 출발점: 수면 시작시간의 평균/ 끝점: 기상시간의 평균\n",
        "2. 중간지점 어디로 잡을 것인지(최빈값, 평균, 중간값 등) / 측정기간 통일 어떻게 할 것인지(짧은 타임에 패딩 or 보간 활용)\n",
        "3. core면 1 아니면 0, 라벨링 달기\n",
        "4. 학습: 우선 머신러닝으로 학습 후 샤프벨류 확인 , 그후 수정님이 짜신 lstm 코드 물리기\n",
        "5. 대드라인: 내일(5/26(금))까지 최빈값, 그 다음 민값, >>자동으로 돌아가게끔 전처리, interpolation 끝내기\n",
        "  -> 결과 잘 안나온다면 왜 안나오는지 확인"
      ],
      "metadata": {
        "id": "yjV8JHk6pv5u"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J3vb7XptPp13"
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      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ? SJ 애플워치 데이터를 이진분류로 사용하기로 한 것인지?"
      ],
      "metadata": {
        "id": "ii7V6lhbqeWj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "sg5Ihutvqhji"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "aGHOMMRhqy8V",
        "outputId": "ae53e8db-ba44-4e8d-880f-bde6278bf90c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SJ 데이터 로드\n",
        "data = pd.read_csv('/content/drive/MyDrive/KDT_project/HACKATHON/data/Sleep_Analysis_Data_sj.csv')\n",
        "data.head()"
      ],
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        "outputId": "28a3574d-8470-4793-f3c6-7cfa46a5c007"
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      "execution_count": 128,
      "outputs": [
        {
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              "                 Start                  End  Duration (hr)  Value  \\\n",
              "0  2023-02-13 23:21:07  2023-02-14 08:47:48          9.440  InBed   \n",
              "1  2023-02-13 23:50:25  2023-02-14 00:07:25          0.283   Core   \n",
              "2  2023-02-14 00:07:25  2023-02-14 00:27:25          0.333   Deep   \n",
              "3  2023-02-14 00:27:25  2023-02-14 00:30:25          0.050   Core   \n",
              "4  2023-02-14 00:30:25  2023-02-14 00:38:25          0.133   Deep   \n",
              "\n",
              "                 Source  \n",
              "0                   NHC  \n",
              "1  Sujeong의 Apple Watch  \n",
              "2  Sujeong의 Apple Watch  \n",
              "3  Sujeong의 Apple Watch  \n",
              "4  Sujeong의 Apple Watch  "
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    {
      "cell_type": "markdown",
      "source": [
        "# ? InBed 데이터를 보는 이유?"
      ],
      "metadata": {
        "id": "Y5r78e-DxfoA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- [Duration] 컬럼 기준으로\n",
        "  - 3/1(공휴일) :  [Value]  == InBed == 4.26hr(d이상치) & [Source] = NHC(핸드폰이름)  -> drop\n",
        "  - 3/21(수요일) : -> 워치 착용하지 않은 날로 drop (하루 전체 [Value] == InBed -> 워치 착용안하고 잤을 때, InBed값으로 채워짐**)**"
      ],
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      "cell_type": "markdown",
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)"
      ],
      "metadata": {
        "id": "EHC_JA2Tu13b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "9RgVARo8uvep"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 하루 수면에 대한 정보가 담긴 InBed 데이터.\n",
        "# [Value] == 'InBed'이고 ['Source']=='NHC'인 데이터 사용.\n",
        "sleep_start = data[(data[\"Value\"]==\"InBed\") & (data['Source']=='NHC')]\n",
        "\n",
        "# Series to datetime\n",
        "# 데이터 타입을 datetime으로 변경. 날짜와 시간 추출이 가능하도록 함.\n",
        "sleep_start['Sl_Start'] = pd.to_datetime(sleep_start['Start'], format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "sleep_start.info()"
      ],
      "metadata": {
        "id": "MacTnGnFwBzv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#slst = sleep_start\n",
        "\n",
        "sleep_start['slst_month']   = sleep_start['Sl_Start'].dt.month\n",
        "sleep_start['slst_day']   = sleep_start['Sl_Start'].dt.day          # 일(숫자)\n",
        "sleep_start['slst_time']  = sleep_start['Sl_Start'].dt.time         # HH:MM:SS(문자)\n",
        "sleep_start['slst_hour']  = sleep_start['Sl_Start'].dt.hour         # 시(숫자)\n",
        "sleep_start['slst_minute'] = sleep_start['Sl_Start'].dt.minute       # 분(숫자)\n",
        "sleep_start['slst_second'] = sleep_start['Sl_Start'].dt.second       # 초(숫자)\n",
        "\n",
        "sleep_start.head()"
      ],
      "metadata": {
        "id": "40-mVvRwxx20"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bpLf0UbZzRqE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "v2xhzt65zRmd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 데이터 전처리"
      ],
      "metadata": {
        "id": "tbltJYia1dQ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IuYYg5BM1e2R",
        "outputId": "060ab23e-bf01-40e1-d1bc-11b212a0c784"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Start                  End  Duration (hr)  Value  \\\n",
              "0  2023-02-13 23:21:07  2023-02-14 08:47:48          9.440  InBed   \n",
              "1  2023-02-13 23:50:25  2023-02-14 00:07:25          0.283   Core   \n",
              "2  2023-02-14 00:07:25  2023-02-14 00:27:25          0.333   Deep   \n",
              "3  2023-02-14 00:27:25  2023-02-14 00:30:25          0.050   Core   \n",
              "4  2023-02-14 00:30:25  2023-02-14 00:38:25          0.133   Deep   \n",
              "\n",
              "                 Source  \n",
              "0                   NHC  \n",
              "1  Sujeong의 Apple Watch  \n",
              "2  Sujeong의 Apple Watch  \n",
              "3  Sujeong의 Apple Watch  \n",
              "4  Sujeong의 Apple Watch  "
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              "      <th>0</th>\n",
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              "      <td>2023-02-14 08:47:48</td>\n",
              "      <td>9.440</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-02-13 23:50:25</td>\n",
              "      <td>2023-02-14 00:07:25</td>\n",
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              "      <th>4</th>\n",
              "      <td>2023-02-14 00:30:25</td>\n",
              "      <td>2023-02-14 00:38:25</td>\n",
              "      <td>0.133</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2888f912-b907-45a7-974b-9a4e6d2ab47a')\"\n",
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              "      </button>\n",
              "      \n",
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              "      <script>\n",
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              "          document.querySelector('#df-2888f912-b907-45a7-974b-9a4e6d2ab47a button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-2888f912-b907-45a7-974b-9a4e6d2ab47a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PCEoIuAE1gG5",
        "outputId": "58e56e74-05ec-4f1a-dde0-cc2b6f97ce09"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Start                  End  Duration (hr)  Value  \\\n",
              "2950  2023-05-03 06:44:05  2023-05-03 07:09:05          0.417    REM   \n",
              "2951  2023-05-03 07:09:05  2023-05-03 07:29:05          0.333   Core   \n",
              "2952  2023-05-03 07:29:05  2023-05-03 07:30:05          0.017  Awake   \n",
              "2953  2023-05-03 07:30:05  2023-05-03 07:31:05          0.017   Core   \n",
              "2954  2023-05-03 07:30:05  2023-05-03 07:31:05          0.017  InBed   \n",
              "\n",
              "                    Source  \n",
              "2950  Sujeong의 Apple Watch  \n",
              "2951  Sujeong의 Apple Watch  \n",
              "2952  Sujeong의 Apple Watch  \n",
              "2953  Sujeong의 Apple Watch  \n",
              "2954  Sujeong의 Apple Watch  "
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              "      <th>2951</th>\n",
              "      <td>2023-05-03 07:09:05</td>\n",
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              "      <th>2953</th>\n",
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              "      <td>0.017</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c052b96b-68f9-40c5-a711-fe33f86c42dc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c052b96b-68f9-40c5-a711-fe33f86c42dc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2023.02.13 ~ 2023.05.03 데이터"
      ],
      "metadata": {
        "id": "DxlegmxW1io_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### [전처리 기준] \n",
        "  - 규칙적인 생활(2/20일 저녁, 2/21 KDT 교육 시작) 데이터 사용, 20일 이전 날짜 drop\n",
        "  - [\"Value\"] != 'InBed'이고 ['Source'] != 'NHC'인 데이터 사용."
      ],
      "metadata": {
        "id": "arflwToZuaKO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 2/24 & 3/20 & 3/25 & 4/21 저녁 워치 착용하지 않고 수면 -> drop"
      ],
      "metadata": {
        "id": "TxYn5ed71Fqa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 이상치 확인\n",
        "## 2/24 & 3/20 & 3/25 & 4/21 저녁 워치 착용 X"
      ],
      "metadata": {
        "id": "poVVv5fU3p_w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start_date = '2023-02-24 12:00:00'\n",
        "end_date = '2023-02-25 12:00:00'\n",
        "tmp = data[(data['Start']>=start_date) & (data['Start']<=end_date)]\n",
        "tmp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "rYnD5XZN2pTe",
        "outputId": "82129bda-fa67-483c-cf31-ec32612e761d"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Start                  End  Duration (hr)  Value Source\n",
              "493  2023-02-25 01:40:05  2023-02-25 07:30:00           5.83  InBed    NHC"
            ],
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>493</th>\n",
              "      <td>2023-02-25 01:40:05</td>\n",
              "      <td>2023-02-25 07:30:00</td>\n",
              "      <td>5.83</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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        "start_date = '2023-03-20 12:00:00'\n",
        "end_date = '2023-03-21 12:00:00'\n",
        "tmp = data[(data['Start']>=start_date) & (data['Start']<=end_date)]\n",
        "tmp"
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              "                    Start                  End  Duration (hr)  Value Source\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    {
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        "end_date = '2023-03-26 12:00:00'\n",
        "tmp = data[(data['Start']>=start_date) & (data['Start']<=end_date)]\n",
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              "                    Start                  End  Duration (hr)  Value Source\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start_date = '2023-04-21 12:00:00'\n",
        "end_date = '2023-04-22 12:00:00'\n",
        "tmp = data[(data['Start']>=start_date) & (data['Start']<=end_date)]\n",
        "tmp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "tOBVSmNGzy22",
        "outputId": "6bd4e148-2fd0-4467-9311-b84160ec57c6"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Start                  End  Duration (hr)  Value Source\n",
              "2582  2023-04-22 00:40:57  2023-04-22 07:30:10           6.82  InBed    NHC"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e3a96d0b-932d-4e71-bd5e-0800906e5561\">\n",
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              "      <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>Start</th>\n",
              "      <th>End</th>\n",
              "      <th>Duration (hr)</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2582</th>\n",
              "      <td>2023-04-22 00:40:57</td>\n",
              "      <td>2023-04-22 07:30:10</td>\n",
              "      <td>6.82</td>\n",
              "      <td>InBed</td>\n",
              "      <td>NHC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3a96d0b-932d-4e71-bd5e-0800906e5561')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e3a96d0b-932d-4e71-bd5e-0800906e5561 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e3a96d0b-932d-4e71-bd5e-0800906e5561');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E2tPfAjBzxVe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 이상치 제거"
      ],
      "metadata": {
        "id": "Cn2Ue8JV3xiL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start_date = '2023-02-20 12:00:00'\n",
        "end_date = '2023-02-24 12:00:00'\n",
        "a = data[(data['Start']>=start_date) & (data['Start']<=end_date)]\n",
        "\n",
        "start_date = '2023-02-25 12:00:00'\n",
        "end_date = '2023-03-20 12:00:00'\n",
        "b = data[(data['Start']>=start_date) & (data['Start']<=end_date)]\n",
        "\n",
        "start_date = '2023-03-21 12:00:00'\n",
        "end_date = '2023-03-25 12:00:00'\n",
        "c = data[(data['Start']>=start_date) & (data['Start']<=end_date)]\n",
        "\n",
        "start_date = '2023-03-26 12:00:00'\n",
        "end_date = '2023-04-21 12:00:00'\n",
        "d = data[(data['Start']>=start_date) & (data['Start']<=end_date)]\n",
        "\n",
        "start_date = '2023-04-22 12:00:00'\n",
        "end_date = '2023-05-03 12:00:00'\n",
        "e = data[(data['Start']>=start_date) & (data['Start']<=end_date)]\n",
        "\n",
        "sj = pd.concat([a, b, c, d, e])\n",
        "sj.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "m7IVN8vctcWX",
        "outputId": "dabb04e3-c965-4c60-f537-df518287d4ff"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Start                  End  Duration (hr)  Value  \\\n",
              "296  2023-02-21 00:18:21  2023-02-21 06:00:23          5.700  InBed   \n",
              "297  2023-02-21 00:24:46  2023-02-21 00:52:16          0.458   Core   \n",
              "298  2023-02-21 00:24:46  2023-02-21 02:28:46          2.070  InBed   \n",
              "299  2023-02-21 00:52:16  2023-02-21 00:57:16          0.083   Deep   \n",
              "300  2023-02-21 00:57:16  2023-02-21 02:03:16          1.100   Core   \n",
              "\n",
              "                   Source  \n",
              "296                   NHC  \n",
              "297  Sujeong의 Apple Watch  \n",
              "298  Sujeong의 Apple Watch  \n",
              "299  Sujeong의 Apple Watch  \n",
              "300  Sujeong의 Apple Watch  "
            ],
            "text/html": [
              "\n",
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              "      <td>5.700</td>\n",
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              "      <td>NHC</td>\n",
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              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>2023-02-21 00:24:46</td>\n",
              "      <td>2023-02-21 00:52:16</td>\n",
              "      <td>0.458</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>2023-02-21 00:24:46</td>\n",
              "      <td>2023-02-21 02:28:46</td>\n",
              "      <td>2.070</td>\n",
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              "    <tr>\n",
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              "      <td>2023-02-21 00:52:16</td>\n",
              "      <td>2023-02-21 00:57:16</td>\n",
              "      <td>0.083</td>\n",
              "      <td>Deep</td>\n",
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              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>2023-02-21 00:57:16</td>\n",
              "      <td>2023-02-21 02:03:16</td>\n",
              "      <td>1.100</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0184488-129e-460b-9d0e-866b3b1c6fb3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f0184488-129e-460b-9d0e-866b3b1c6fb3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f0184488-129e-460b-9d0e-866b3b1c6fb3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# [\"Value\"] != 'InBed'인 데이터 사용.\n",
        "sj = sj[sj['Value'] != \"InBed\"]\n",
        "sj.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pZLHd4857ih3",
        "outputId": "b8bb1d4c-d583-4133-9eb0-0edff1fc826a"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Start                  End  Duration (hr)  Value  \\\n",
              "297  2023-02-21 00:24:46  2023-02-21 00:52:16          0.458   Core   \n",
              "299  2023-02-21 00:52:16  2023-02-21 00:57:16          0.083   Deep   \n",
              "300  2023-02-21 00:57:16  2023-02-21 02:03:16          1.100   Core   \n",
              "301  2023-02-21 02:03:16  2023-02-21 02:28:46          0.425   Deep   \n",
              "302  2023-02-21 02:28:46  2023-02-21 02:29:16          0.008  Awake   \n",
              "\n",
              "                   Source  \n",
              "297  Sujeong의 Apple Watch  \n",
              "299  Sujeong의 Apple Watch  \n",
              "300  Sujeong의 Apple Watch  \n",
              "301  Sujeong의 Apple Watch  \n",
              "302  Sujeong의 Apple Watch  "
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              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>301</th>\n",
              "      <td>2023-02-21 02:03:16</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-425f67e8-2e5c-4c13-8b42-441493e084de')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-425f67e8-2e5c-4c13-8b42-441493e084de button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-425f67e8-2e5c-4c13-8b42-441493e084de');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Series to datetime\n",
        "# 데이터 타입을 datetime으로 변경한 \"Sl_start\" 열 추가. 날짜와 시간 추출이 가능하도록 함.\n",
        "# datetime64[ns] 타입\n",
        "sj['Sl_start'] = pd.to_datetime(sj['Start'], format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "sj.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1acPcDG4yQr",
        "outputId": "104f2334-cecf-4408-fed0-aa78bf438c2e"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2039 entries, 297 to 2953\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   Start          2039 non-null   object        \n",
            " 1   End            2039 non-null   object        \n",
            " 2   Duration (hr)  2039 non-null   float64       \n",
            " 3   Value          2039 non-null   object        \n",
            " 4   Source         2039 non-null   object        \n",
            " 5   Sl_start       2039 non-null   datetime64[ns]\n",
            "dtypes: datetime64[ns](1), float64(1), object(4)\n",
            "memory usage: 111.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 주중 데이터셋 생성"
      ],
      "metadata": {
        "id": "h7zbJqKd5TDW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "  - 주중데이터만 이용\n",
        "  - 주말은 나중에 따로"
      ],
      "metadata": {
        "id": "txhcEFiH3jyB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 요일 정보를 담은 \"weekday\" 열 생성. \n",
        "# [int64]  타입\n",
        "# Monday=0, Sunday=6.\n",
        "import datetime\n",
        "\n",
        "sj[\"weekday\"] = sj['Sl_start'].dt.weekday\n",
        "sj.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vYMtNLIH5CLq",
        "outputId": "cf1ea7de-c309-45ce-b9e6-e6defb5157d7"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Start                  End  Duration (hr)  Value  \\\n",
              "297  2023-02-21 00:24:46  2023-02-21 00:52:16          0.458   Core   \n",
              "299  2023-02-21 00:52:16  2023-02-21 00:57:16          0.083   Deep   \n",
              "300  2023-02-21 00:57:16  2023-02-21 02:03:16          1.100   Core   \n",
              "301  2023-02-21 02:03:16  2023-02-21 02:28:46          0.425   Deep   \n",
              "302  2023-02-21 02:28:46  2023-02-21 02:29:16          0.008  Awake   \n",
              "\n",
              "                   Source            Sl_start  weekday  \n",
              "297  Sujeong의 Apple Watch 2023-02-21 00:24:46        1  \n",
              "299  Sujeong의 Apple Watch 2023-02-21 00:52:16        1  \n",
              "300  Sujeong의 Apple Watch 2023-02-21 00:57:16        1  \n",
              "301  Sujeong의 Apple Watch 2023-02-21 02:03:16        1  \n",
              "302  Sujeong의 Apple Watch 2023-02-21 02:28:46        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9e0bad28-d990-4f2c-a8e2-a9580268eeca\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\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>Start</th>\n",
              "      <th>End</th>\n",
              "      <th>Duration (hr)</th>\n",
              "      <th>Value</th>\n",
              "      <th>Source</th>\n",
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              "      <th>weekday</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>2023-02-21 00:24:46</td>\n",
              "      <td>2023-02-21 00:52:16</td>\n",
              "      <td>0.458</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-02-21 00:24:46</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>2023-02-21 00:52:16</td>\n",
              "      <td>2023-02-21 00:57:16</td>\n",
              "      <td>0.083</td>\n",
              "      <td>Deep</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-02-21 00:52:16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>2023-02-21 00:57:16</td>\n",
              "      <td>2023-02-21 02:03:16</td>\n",
              "      <td>1.100</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-02-21 00:57:16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>2023-02-21 02:03:16</td>\n",
              "      <td>2023-02-21 02:28:46</td>\n",
              "      <td>0.425</td>\n",
              "      <td>Deep</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-02-21 02:03:16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>2023-02-21 02:28:46</td>\n",
              "      <td>2023-02-21 02:29:16</td>\n",
              "      <td>0.008</td>\n",
              "      <td>Awake</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-02-21 02:28:46</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e0bad28-d990-4f2c-a8e2-a9580268eeca')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9e0bad28-d990-4f2c-a8e2-a9580268eeca button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9e0bad28-d990-4f2c-a8e2-a9580268eeca');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sj['weekday'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1jbiePfT6jk4",
        "outputId": "e50d4749-62ee-4723-8ab7-a639c65a9790"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    308\n",
              "6    307\n",
              "1    305\n",
              "3    296\n",
              "5    281\n",
              "2    277\n",
              "0    265\n",
              "Name: weekday, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 주중 데이터셋 'weekday' 생성\n",
        "weekday = sj[sj['weekday'].between(0,4)]\n",
        "weekday['weekday'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYNPTnP95xvh",
        "outputId": "6c9ec7c5-2846-4152-a258-b0fbbe2d68fc"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    308\n",
              "1    305\n",
              "3    296\n",
              "2    277\n",
              "0    265\n",
              "Name: weekday, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 이진 분류 데이터셋으로 변경\n",
        "### [\"Value\"] == 'Core' 면 1 아니면 0, 라벨링 달기"
      ],
      "metadata": {
        "id": "ZFbxslNr8JHe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weekday['Value'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5QBxtPWX84H7",
        "outputId": "f063e3e6-7ff6-4a3f-809a-af5620e54ac7"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Core     674\n",
              "Awake    323\n",
              "REM      310\n",
              "Deep     144\n",
              "Name: Value, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# \"Source\" 열의 데이터를 레이블링 해줌.\n",
        "\n",
        "weekday['Value']= weekday['Value'].replace('Deep',0)\n",
        "weekday['Value']= weekday['Value'].replace('Core',1)\n",
        "weekday['Value']= weekday['Value'].replace('REM',0)\n",
        "weekday['Value']= weekday['Value'].replace('Awake',0)\n",
        "\n",
        "weekday['Value'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uvvLlHjE8DyN",
        "outputId": "6007e765-40a3-47dd-84bb-6d947070fbe4"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-141-3323a29a578b>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  weekday['Value']= weekday['Value'].replace('Deep',0)\n",
            "<ipython-input-141-3323a29a578b>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  weekday['Value']= weekday['Value'].replace('Core',1)\n",
            "<ipython-input-141-3323a29a578b>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  weekday['Value']= weekday['Value'].replace('REM',0)\n",
            "<ipython-input-141-3323a29a578b>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  weekday['Value']= weekday['Value'].replace('Awake',0)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    777\n",
              "1    674\n",
              "Name: Value, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jcH4etgL9306"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 출발점: 수면 시작시간의 평균/ 끝점: 기상시간의 평균 구하기"
      ],
      "metadata": {
        "id": "dUzxM48hANr6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 날짜별로 데이터 저장하고 불러와서 출발점과 끝점을 구하고자 함.\n",
        "## 주말과 주중은 우선 만들고 수작업으로 분리함.(ㅠ)"
      ],
      "metadata": {
        "id": "bnkrZDOcL5MX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import datetime\n",
        "\n",
        "# 이름 저장을 위해 월별로 구분함\n",
        "start_date = '2023-02-20 12:00:00'\n",
        "end_date = '2023-02-24 12:00:00'\n",
        "start_date = pd.to_datetime(start_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "end_date = pd.to_datetime(end_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "\n",
        "# 2월 데이터를 tmp로 저장.\n",
        "# \"NHC\"와 'InBed'를 모두 제외한 데이터.\n",
        "tmp = sj[(sj['Sl_start']>=start_date) & (sj['Sl_start']<=end_date)]\n",
        "\n",
        "# 2월 21일부터 시작.\n",
        "d = 21  # 21일\n",
        "date_diff = end_date - start_date   # 날짜 차이만큼 반복해서 데이터를 저장할 것이기 때문.\n",
        "rep = date_diff.days   # 반복할 횟수(날)만 추출\n",
        "file_path = \"/content/drive/MyDrive/KDT_project/HACKATHON/data/day_split\"   # 데이터를 저장할 경로\n",
        "os.chdir(file_path)\n",
        "\n",
        "loop_date_1 = start_date  # 하루 수면패턴 추출을 위한 시작 date\n",
        "\n",
        "for i in range(rep):\n",
        "  days = datetime.timedelta(days=1)\n",
        "  loop_date_2 = loop_date_1 + days     # 하루 수면패턴 추출을 위한 끝 date\n",
        "  loop = tmp[(tmp['Sl_start']>=loop_date_1) & (tmp['Sl_start']<=loop_date_2)]  # 하루 수면패턴 추출.\n",
        "\n",
        "  dst = \"Feb_\" + str(d) + '.csv'\n",
        "  loop.to_csv(dst)\n",
        "\n",
        "  loop_date_1 = loop_date_2\n",
        "  d += 1"
      ],
      "metadata": {
        "id": "DbcLT-NyARmw"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import datetime\n",
        "\n",
        "# 이름 저장을 위해 월별로 구분함\n",
        "start_date = '2023-02-25 12:00:00'\n",
        "end_date = '2023-02-28 12:00:00'\n",
        "start_date = pd.to_datetime(start_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "end_date = pd.to_datetime(end_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "\n",
        "tmp = sj[(sj['Sl_start']>=start_date) & (sj['Sl_start']<=end_date)]\n",
        "\n",
        "# 2월 26일부터 시작.\n",
        "d = 26  # 26일\n",
        "date_diff = end_date - start_date   # 날짜 차이만큼 반복해서 데이터를 저장할 것이기 때문.\n",
        "rep = date_diff.days   # 반복할 횟수(날)만 추출\n",
        "file_path = \"/content/drive/MyDrive/KDT_project/HACKATHON/data/day_split\"   # 데이터를 저장할 경로\n",
        "os.chdir(file_path)\n",
        "\n",
        "loop_date_1 = start_date  # 하루 수면패턴 추출을 위한 시작 date\n",
        "\n",
        "for i in range(rep):\n",
        "  days = datetime.timedelta(days=1)\n",
        "  loop_date_2 = loop_date_1 + days     # 하루 수면패턴 추출을 위한 끝 date\n",
        "  loop = tmp[(tmp['Sl_start']>=loop_date_1) & (tmp['Sl_start']<=loop_date_2)]  # 하루 수면패턴 추출.\n",
        "\n",
        "  dst = \"Feb_\" + str(d) + '.csv'\n",
        "  loop.to_csv(dst)\n",
        "\n",
        "  loop_date_1 = loop_date_2\n",
        "  d += 1"
      ],
      "metadata": {
        "id": "HMfTzCua93wV"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import datetime\n",
        "\n",
        "# 이름 저장을 위해 월별로 구분함\n",
        "start_date = '2023-02-28 12:00:00'\n",
        "end_date = '2023-03-20 12:00:00'\n",
        "start_date = pd.to_datetime(start_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "end_date = pd.to_datetime(end_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "\n",
        "tmp = sj[(sj['Sl_start']>=start_date) & (sj['Sl_start']<=end_date)]\n",
        "\n",
        "# 3월 1일부터 시작.\n",
        "d = 1\n",
        "date_diff = end_date - start_date   # 날짜 차이만큼 반복해서 데이터를 저장할 것이기 때문.\n",
        "rep = date_diff.days   # 반복할 횟수(날)만 추출\n",
        "file_path = \"/content/drive/MyDrive/KDT_project/HACKATHON/data/day_split\"   # 데이터를 저장할 경로\n",
        "os.chdir(file_path)\n",
        "\n",
        "loop_date_1 = start_date  # 하루 수면패턴 추출을 위한 시작 date\n",
        "\n",
        "for i in range(rep):\n",
        "  days = datetime.timedelta(days=1)\n",
        "  loop_date_2 = loop_date_1 + days     # 하루 수면패턴 추출을 위한 끝 date\n",
        "  loop = tmp[(tmp['Sl_start']>=loop_date_1) & (tmp['Sl_start']<=loop_date_2)]  # 하루 수면패턴 추출.\n",
        "\n",
        "  dst = \"March_\" + str(d) + '.csv'\n",
        "  loop.to_csv(dst)\n",
        "\n",
        "  loop_date_1 = loop_date_2\n",
        "  d += 1"
      ],
      "metadata": {
        "id": "afgsg_jhC2BT"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import datetime\n",
        "\n",
        "# 이름 저장을 위해 월별로 구분함\n",
        "start_date = '2023-03-21 12:00:00'\n",
        "end_date = '2023-03-25 12:00:00'\n",
        "start_date = pd.to_datetime(start_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "end_date = pd.to_datetime(end_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "\n",
        "tmp = sj[(sj['Sl_start']>=start_date) & (sj['Sl_start']<=end_date)]\n",
        "\n",
        "# 3월 22일부터 시작.\n",
        "d = 22\n",
        "date_diff = end_date - start_date   # 날짜 차이만큼 반복해서 데이터를 저장할 것이기 때문.\n",
        "rep = date_diff.days   # 반복할 횟수(날)만 추출\n",
        "file_path = \"/content/drive/MyDrive/KDT_project/HACKATHON/data/day_split\"   # 데이터를 저장할 경로\n",
        "os.chdir(file_path)\n",
        "\n",
        "loop_date_1 = start_date  # 하루 수면패턴 추출을 위한 시작 date\n",
        "\n",
        "for i in range(rep):\n",
        "  days = datetime.timedelta(days=1)\n",
        "  loop_date_2 = loop_date_1 + days     # 하루 수면패턴 추출을 위한 끝 date\n",
        "  loop = tmp[(tmp['Sl_start']>=loop_date_1) & (tmp['Sl_start']<=loop_date_2)]  # 하루 수면패턴 추출.\n",
        "\n",
        "  dst = \"March_\" + str(d) + '.csv'\n",
        "  loop.to_csv(dst)\n",
        "\n",
        "  loop_date_1 = loop_date_2\n",
        "  d += 1"
      ],
      "metadata": {
        "id": "KJd6xj5_DOqi"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import datetime\n",
        "\n",
        "# 이름 저장을 위해 월별로 구분함\n",
        "start_date = '2023-03-26 12:00:00'\n",
        "end_date = '2023-03-31 12:00:00'\n",
        "start_date = pd.to_datetime(start_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "end_date = pd.to_datetime(end_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "\n",
        "tmp = sj[(sj['Sl_start']>=start_date) & (sj['Sl_start']<=end_date)]\n",
        "\n",
        "# 3월 27일부터 시작.\n",
        "d = 27\n",
        "date_diff = end_date - start_date   # 날짜 차이만큼 반복해서 데이터를 저장할 것이기 때문.\n",
        "rep = date_diff.days   # 반복할 횟수(날)만 추출\n",
        "file_path = \"/content/drive/MyDrive/KDT_project/HACKATHON/data/day_split\"   # 데이터를 저장할 경로\n",
        "os.chdir(file_path)\n",
        "\n",
        "loop_date_1 = start_date  # 하루 수면패턴 추출을 위한 시작 date\n",
        "\n",
        "for i in range(rep):\n",
        "  days = datetime.timedelta(days=1)\n",
        "  loop_date_2 = loop_date_1 + days     # 하루 수면패턴 추출을 위한 끝 date\n",
        "  loop = tmp[(tmp['Sl_start']>=loop_date_1) & (tmp['Sl_start']<=loop_date_2)]  # 하루 수면패턴 추출.\n",
        "\n",
        "  dst = \"March_\" + str(d) + '.csv'\n",
        "  loop.to_csv(dst)\n",
        "\n",
        "  loop_date_1 = loop_date_2\n",
        "  d += 1"
      ],
      "metadata": {
        "id": "lo-jGngVEFXk"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import datetime\n",
        "\n",
        "# 이름 저장을 위해 월별로 구분함\n",
        "start_date = '2023-03-31 12:00:00'\n",
        "end_date = '2023-04-21 12:00:00'\n",
        "start_date = pd.to_datetime(start_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "end_date = pd.to_datetime(end_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "\n",
        "tmp = sj[(sj['Sl_start']>=start_date) & (sj['Sl_start']<=end_date)]\n",
        "\n",
        "# 4월 1일부터 시작.\n",
        "d = 1\n",
        "date_diff = end_date - start_date   # 날짜 차이만큼 반복해서 데이터를 저장할 것이기 때문.\n",
        "rep = date_diff.days   # 반복할 횟수(날)만 추출\n",
        "file_path = \"/content/drive/MyDrive/KDT_project/HACKATHON/data/day_split\"   # 데이터를 저장할 경로\n",
        "os.chdir(file_path)\n",
        "\n",
        "loop_date_1 = start_date  # 하루 수면패턴 추출을 위한 시작 date\n",
        "\n",
        "for i in range(rep):\n",
        "  days = datetime.timedelta(days=1)\n",
        "  loop_date_2 = loop_date_1 + days     # 하루 수면패턴 추출을 위한 끝 date\n",
        "  loop = tmp[(tmp['Sl_start']>=loop_date_1) & (tmp['Sl_start']<=loop_date_2)]  # 하루 수면패턴 추출.\n",
        "\n",
        "  dst = \"April_\" + str(d) + '.csv'\n",
        "  loop.to_csv(dst)\n",
        "\n",
        "  loop_date_1 = loop_date_2\n",
        "  d += 1"
      ],
      "metadata": {
        "id": "0pCGUdMkEPEX"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import datetime\n",
        "\n",
        "# 이름 저장을 위해 월별로 구분함\n",
        "start_date = '2023-04-22 12:00:00'\n",
        "end_date = '2023-04-30 12:00:00'\n",
        "start_date = pd.to_datetime(start_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "end_date = pd.to_datetime(end_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "\n",
        "tmp = sj[(sj['Sl_start']>=start_date) & (sj['Sl_start']<=end_date)]\n",
        "\n",
        "# 4월 23일부터 시작.\n",
        "d = 23\n",
        "date_diff = end_date - start_date   # 날짜 차이만큼 반복해서 데이터를 저장할 것이기 때문.\n",
        "rep = date_diff.days   # 반복할 횟수(날)만 추출\n",
        "file_path = \"/content/drive/MyDrive/KDT_project/HACKATHON/data/day_split\"   # 데이터를 저장할 경로\n",
        "os.chdir(file_path)\n",
        "\n",
        "loop_date_1 = start_date  # 하루 수면패턴 추출을 위한 시작 date\n",
        "\n",
        "for i in range(rep):\n",
        "  days = datetime.timedelta(days=1)\n",
        "  loop_date_2 = loop_date_1 + days     # 하루 수면패턴 추출을 위한 끝 date\n",
        "  loop = tmp[(tmp['Sl_start']>=loop_date_1) & (tmp['Sl_start']<=loop_date_2)]  # 하루 수면패턴 추출.\n",
        "\n",
        "  dst = \"April_\" + str(d) + '.csv'\n",
        "  loop.to_csv(dst)\n",
        "\n",
        "  loop_date_1 = loop_date_2\n",
        "  d += 1"
      ],
      "metadata": {
        "id": "CmcEQ35ZEY8H"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import datetime\n",
        "\n",
        "# 이름 저장을 위해 월별로 구분함\n",
        "start_date = '2023-04-30 12:00:00'\n",
        "end_date = '2023-05-03 12:00:00'\n",
        "start_date = pd.to_datetime(start_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "end_date = pd.to_datetime(end_date, format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "\n",
        "tmp = sj[(sj['Sl_start']>=start_date) & (sj['Sl_start']<=end_date)]\n",
        "\n",
        "# 5월 1일부터 시작.\n",
        "d = 1\n",
        "date_diff = end_date - start_date   # 날짜 차이만큼 반복해서 데이터를 저장할 것이기 때문.\n",
        "rep = date_diff.days   # 반복할 횟수(날)만 추출\n",
        "file_path = \"/content/drive/MyDrive/KDT_project/HACKATHON/data/day_split\"   # 데이터를 저장할 경로\n",
        "os.chdir(file_path)\n",
        "\n",
        "loop_date_1 = start_date  # 하루 수면패턴 추출을 위한 시작 date\n",
        "\n",
        "for i in range(rep):\n",
        "  days = datetime.timedelta(days=1)\n",
        "  loop_date_2 = loop_date_1 + days     # 하루 수면패턴 추출을 위한 끝 date\n",
        "  loop = tmp[(tmp['Sl_start']>=loop_date_1) & (tmp['Sl_start']<=loop_date_2)]  # 하루 수면패턴 추출.\n",
        "\n",
        "  dst = \"May_\" + str(d) + '.csv'\n",
        "  loop.to_csv(dst)\n",
        "\n",
        "  loop_date_1 = loop_date_2\n",
        "  d += 1"
      ],
      "metadata": {
        "id": "s_Y0yrNwEiW-"
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tzq45lbxKIRj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ILT5zQ5RMDne"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 주중 데이터 불러오기"
      ],
      "metadata": {
        "id": "yCgqDNxyMG3S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## 해당 경로에 있는 .csv 파일명 리스트 가져오기\n",
        "\n",
        "import os\n",
        "\n",
        "path = '/content/drive/MyDrive/KDT_project/HACKATHON/data/day_split_weekday'\n",
        "file_list = os.listdir(path)\n",
        "file_name = [file for file in file_list if file.endswith('.csv')] ## 파일명 끝이 .csv인 경우"
      ],
      "metadata": {
        "id": "_9AWwWxNMKYb"
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/drive/MyDrive/KDT_project/HACKATHON/data/day_split_weekday"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MzKd1hCUMKKF",
        "outputId": "18d6a244-4ccc-45e4-d867-6751999fa082"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/KDT_project/HACKATHON/data/day_split_weekday\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_weekday = pd.DataFrame()\n",
        "\n",
        "for files in file_name:\n",
        "    df= pd.read_csv(files)\n",
        "    data_weekday = pd.concat([data_weekday, df])"
      ],
      "metadata": {
        "id": "DdwgoCJWOv9-"
      },
      "execution_count": 189,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_weekday"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "QT4AKN88PBYi",
        "outputId": "11c69e89-ed72-4d14-fef4-4edc1b7d628b"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Unnamed: 0                Start                  End  Duration (hr)  \\\n",
              "0          297  2023-02-21 00:24:46  2023-02-21 00:52:16          0.458   \n",
              "1          299  2023-02-21 00:52:16  2023-02-21 00:57:16          0.083   \n",
              "2          300  2023-02-21 00:57:16  2023-02-21 02:03:16          1.100   \n",
              "3          301  2023-02-21 02:03:16  2023-02-21 02:28:46          0.425   \n",
              "4          302  2023-02-21 02:28:46  2023-02-21 02:29:16          0.008   \n",
              "..         ...                  ...                  ...            ...   \n",
              "16        2948  2023-05-03 06:16:05  2023-05-03 06:44:05          0.467   \n",
              "17        2950  2023-05-03 06:44:05  2023-05-03 07:09:05          0.417   \n",
              "18        2951  2023-05-03 07:09:05  2023-05-03 07:29:05          0.333   \n",
              "19        2952  2023-05-03 07:29:05  2023-05-03 07:30:05          0.017   \n",
              "20        2953  2023-05-03 07:30:05  2023-05-03 07:31:05          0.017   \n",
              "\n",
              "    Value                Source             Sl_start  weekday  \n",
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          "metadata": {},
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    {
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      "source": [
        "data_weekday = data_weekday.drop('Unnamed: 0', axis=1)\n",
        "data_weekday   "
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        "outputId": "ffaab939-aa11-4583-adb1-830dd44784c7"
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      "execution_count": 191,
      "outputs": [
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              "                  Start                  End  Duration (hr)  Value  \\\n",
              "0   2023-02-21 00:24:46  2023-02-21 00:52:16          0.458   Core   \n",
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              "\n",
              "                  Source             Sl_start  weekday  \n",
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              "4   Sujeong의 Apple Watch  2023-02-21 02:28:46        1  \n",
              "..                   ...                  ...      ...  \n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 출발점(잠드는 시간) 데이터셋 생성 \"weekday_start\"\n",
        "weekday_start = data_weekday.loc[0]\n",
        "weekday_start"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iR4bWSHiR_Dt",
        "outputId": "2e4fd6f9-998e-4937-a8dd-71e84ace7e7a"
      },
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                 Source             Sl_start  weekday  \n",
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              "      <th>0</th>\n",
              "      <td>2023-03-13 02:02:06</td>\n",
              "      <td>2023-03-13 02:03:36</td>\n",
              "      <td>0.025</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-13 02:02:06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-13 23:55:24</td>\n",
              "      <td>2023-03-14 00:11:54</td>\n",
              "      <td>0.275</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-13 23:55:24</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-15 00:04:28</td>\n",
              "      <td>2023-03-15 00:23:58</td>\n",
              "      <td>0.325</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-15 00:04:28</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-15 23:50:47</td>\n",
              "      <td>2023-03-16 00:08:17</td>\n",
              "      <td>0.292</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-15 23:50:47</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-16 23:46:34</td>\n",
              "      <td>2023-03-17 00:07:04</td>\n",
              "      <td>0.342</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-16 23:46:34</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-20 00:40:05</td>\n",
              "      <td>2023-03-20 00:45:35</td>\n",
              "      <td>0.092</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-20 00:40:05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-21 22:37:04</td>\n",
              "      <td>2023-03-21 22:40:34</td>\n",
              "      <td>0.058</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-21 22:37:04</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-23 00:30:33</td>\n",
              "      <td>2023-03-23 00:58:33</td>\n",
              "      <td>0.467</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-23 00:30:33</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-23 22:44:34</td>\n",
              "      <td>2023-03-23 23:03:34</td>\n",
              "      <td>0.317</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-23 22:44:34</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-26 23:58:09</td>\n",
              "      <td>2023-03-27 00:00:09</td>\n",
              "      <td>0.033</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-26 23:58:09</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-27 22:31:08</td>\n",
              "      <td>2023-03-27 22:41:08</td>\n",
              "      <td>0.167</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-27 22:31:08</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-28 22:56:35</td>\n",
              "      <td>2023-03-28 23:14:05</td>\n",
              "      <td>0.292</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-28 22:56:35</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-30 00:41:41</td>\n",
              "      <td>2023-03-30 00:44:41</td>\n",
              "      <td>0.050</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-30 00:41:41</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-03-30 22:51:24</td>\n",
              "      <td>2023-03-30 23:00:54</td>\n",
              "      <td>0.158</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-03-30 22:51:24</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-02 23:44:17</td>\n",
              "      <td>2023-04-02 23:46:47</td>\n",
              "      <td>0.042</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-02 23:44:17</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-03 23:36:11</td>\n",
              "      <td>2023-04-03 23:37:41</td>\n",
              "      <td>0.025</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-03 23:36:11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-04 23:12:06</td>\n",
              "      <td>2023-04-04 23:20:06</td>\n",
              "      <td>0.133</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-04 23:12:06</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-05 23:09:04</td>\n",
              "      <td>2023-04-05 23:11:34</td>\n",
              "      <td>0.042</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-05 23:09:04</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-06 23:51:52</td>\n",
              "      <td>2023-04-07 00:16:52</td>\n",
              "      <td>0.417</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-06 23:51:52</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-10 01:22:05</td>\n",
              "      <td>2023-04-10 01:39:35</td>\n",
              "      <td>0.292</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-10 01:22:05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-10 23:04:04</td>\n",
              "      <td>2023-04-10 23:24:34</td>\n",
              "      <td>0.342</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-10 23:04:04</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-11 22:57:16</td>\n",
              "      <td>2023-04-11 23:09:46</td>\n",
              "      <td>0.208</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-11 22:57:16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-13 00:15:30</td>\n",
              "      <td>2023-04-13 00:46:30</td>\n",
              "      <td>0.517</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-13 00:15:30</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-14 00:31:31</td>\n",
              "      <td>2023-04-14 00:33:31</td>\n",
              "      <td>0.033</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-14 00:31:31</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-17 00:26:37</td>\n",
              "      <td>2023-04-17 00:27:37</td>\n",
              "      <td>0.017</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-17 00:26:37</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-17 23:11:10</td>\n",
              "      <td>2023-04-17 23:26:10</td>\n",
              "      <td>0.250</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-17 23:11:10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-18 22:38:41</td>\n",
              "      <td>2023-04-18 22:52:41</td>\n",
              "      <td>0.233</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-18 22:38:41</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-20 00:32:05</td>\n",
              "      <td>2023-04-20 00:33:05</td>\n",
              "      <td>0.017</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-20 00:32:05</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-21 00:41:08</td>\n",
              "      <td>2023-04-21 01:09:08</td>\n",
              "      <td>0.467</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-21 00:41:08</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-23 23:55:14</td>\n",
              "      <td>2023-04-23 23:56:14</td>\n",
              "      <td>0.017</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-23 23:55:14</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-25 00:32:49</td>\n",
              "      <td>2023-04-25 00:38:19</td>\n",
              "      <td>0.092</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-25 00:32:49</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-26 00:08:44</td>\n",
              "      <td>2023-04-26 00:30:14</td>\n",
              "      <td>0.358</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-26 00:08:44</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-26 23:32:04</td>\n",
              "      <td>2023-04-26 23:49:34</td>\n",
              "      <td>0.292</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-26 23:32:04</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-04-28 01:04:20</td>\n",
              "      <td>2023-04-28 01:06:20</td>\n",
              "      <td>0.033</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-04-28 01:04:20</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-05-01 00:38:56</td>\n",
              "      <td>2023-05-01 00:41:26</td>\n",
              "      <td>0.042</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-05-01 00:38:56</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-05-02 01:29:16</td>\n",
              "      <td>2023-05-02 01:49:46</td>\n",
              "      <td>0.342</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-05-02 01:29:16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-05-02 23:58:05</td>\n",
              "      <td>2023-05-02 23:59:05</td>\n",
              "      <td>0.017</td>\n",
              "      <td>Core</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-05-02 23:58:05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d1b48eb-5675-42e7-8d60-fe1fd61a1c68')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "        async function convertToInteractive(key) {\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekday.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zeKjL1DVTQlk",
        "outputId": "b0112643-73f9-4637-dacf-ba9d6cebc5b9"
      },
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1451 entries, 297 to 2953\n",
            "Data columns (total 7 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   Start          1451 non-null   object        \n",
            " 1   End            1451 non-null   object        \n",
            " 2   Duration (hr)  1451 non-null   float64       \n",
            " 3   Value          1451 non-null   int64         \n",
            " 4   Source         1451 non-null   object        \n",
            " 5   Sl_start       1451 non-null   datetime64[ns]\n",
            " 6   weekday        1451 non-null   int64         \n",
            "dtypes: datetime64[ns](1), float64(1), int64(2), object(3)\n",
            "memory usage: 90.7+ KB\n"
          ]
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    },
    {
      "cell_type": "code",
      "source": [
        "weekday_start['Sl_start'] = pd.to_datetime(weekday_start['Sl_start'], format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "weekday_start['slst_time']  = weekday_start['Sl_start'].dt.time         # HH:MM:SS(문자)\n",
        "weekday_start.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "_me5ykeiNJwC",
        "outputId": "319e724b-6886-43e5-9597-31338cb7f800"
      },
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-204-a734cdf81876>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  weekday_start['slst_time']  = weekday_start['Sl_start'].dt.time         # HH:MM:SS(문자)\n"
          ]
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              "                 Start                  End  Duration (hr) Value  \\\n",
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              "0  2023-02-22 00:29:17  2023-02-22 00:47:17          0.300  Core   \n",
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              "0  2023-02-26 23:36:12  2023-02-26 23:57:42          0.358  Core   \n",
              "\n",
              "                 Source            Sl_start  weekday slst_time  \n",
              "0  Sujeong의 Apple Watch 2023-02-21 00:24:46        1  00:24:46  \n",
              "0  Sujeong의 Apple Watch 2023-02-22 00:29:17        2  00:29:17  \n",
              "0  Sujeong의 Apple Watch 2023-02-23 00:19:53        3  00:19:53  \n",
              "0  Sujeong의 Apple Watch 2023-02-23 22:54:10        3  22:54:10  \n",
              "0  Sujeong의 Apple Watch 2023-02-26 23:36:12        6  23:36:12  "
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        "tmp = weekday_start.set_index('Sl_start')\n",
        "tmp.head()"
      ],
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        "colab": {
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          "height": 237
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        "id": "qShkRjPOTG_0",
        "outputId": "9fdc3749-17a0-4a40-fbb9-fbca79d49681"
      },
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   Start                  End  Duration (hr)  \\\n",
              "Sl_start                                                                       \n",
              "2023-02-21 00:24:46  2023-02-21 00:24:46  2023-02-21 00:52:16          0.458   \n",
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              "                    Value                Source  weekday slst_time  \n",
              "Sl_start                                                            \n",
              "2023-02-21 00:24:46  Core  Sujeong의 Apple Watch        1  00:24:46  \n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ! 시간 평균 계산하는 방법 이어서 하기"
      ],
      "metadata": {
        "id": "plwYRaTgUfsL"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K4LmvObUUUMq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OzHrIgQhUEfy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 시간 별로 평균 내기\n",
        "# resample"
      ],
      "metadata": {
        "id": "ctEt38RU-1L-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tmp = weekday.set_index('Sl_start')\n",
        "tmp.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 237
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        "id": "CBQ-39Ku93q-",
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              "                                   Start                  End  Duration (hr)  \\\n",
              "Sl_start                                                                       \n",
              "2023-02-21 00:24:46  2023-02-21 00:24:46  2023-02-21 00:52:16          0.458   \n",
              "2023-02-21 00:52:16  2023-02-21 00:52:16  2023-02-21 00:57:16          0.083   \n",
              "2023-02-21 00:57:16  2023-02-21 00:57:16  2023-02-21 02:03:16          1.100   \n",
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              "2023-02-21 02:28:46  2023-02-21 02:28:46  2023-02-21 02:29:16          0.008   \n",
              "\n",
              "                     Value                Source  weekday  \n",
              "Sl_start                                                   \n",
              "2023-02-21 00:24:46      1  Sujeong의 Apple Watch        1  \n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime\n",
        "newDf = tmp.resample(rule='H').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvrryV_c8eWs",
        "outputId": "677bdc0e-5d63-4eb2-cb2e-4aed8bc0c1c2"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-86-de9cfc73883a>:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  newDf = tmp.resample(rule='H').mean()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "newDf.head(50)\n",
        "\n",
        "\n",
        "# 2023-02-21 00:57:16\t2023-02-21 02:03:16 의 경우 01:00:00이 NaN이 되는 치명적인 문제 발생.."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QnuYBS0_-hIv",
        "outputId": "768b873e-f2f5-45cd-cda2-337fb86e88f8"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Duration (hr)     Value  weekday\n",
              "Sl_start                                             \n",
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              "2023-02-21 01:00:00            NaN       NaN      NaN\n",
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              "2023-02-22 22:00:00            NaN       NaN      NaN\n",
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              "      <th>2023-02-22 01:00:00</th>\n",
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              "      <td>0.428571</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>2023-02-22 02:00:00</th>\n",
              "      <td>0.342500</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>2023-02-22 03:00:00</th>\n",
              "      <td>0.264000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 04:00:00</th>\n",
              "      <td>0.522500</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 05:00:00</th>\n",
              "      <td>0.289250</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 06:00:00</th>\n",
              "      <td>0.068167</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 07:00:00</th>\n",
              "      <td>0.106833</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 08:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 09:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 10:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 11:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 12:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 13:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 14:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 15:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 16:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 17:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 18:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 19:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 20:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 21:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 22:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-22 23:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-23 00:00:00</th>\n",
              "      <td>0.454000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-02-23 01:00:00</th>\n",
              "      <td>0.767000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c1461dd-524e-4a76-8954-fae57cb37923')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5c1461dd-524e-4a76-8954-fae57cb37923 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5c1461dd-524e-4a76-8954-fae57cb37923');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime\n",
        "newDf_D = tmp.resample(rule='D').mean()\n",
        "newDf_D.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "6OKZ_Hia-FK-",
        "outputId": "bbe05650-38d3-4442-f336-2d2e718c0537"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-88-f09ed899303a>:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  newDf_D = tmp.resample(rule='D').mean()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Duration (hr)     Value  weekday\n",
              "Sl_start                                    \n",
              "2023-02-21       0.222480  0.440000      1.0\n",
              "2023-02-22       0.210235  0.441176      2.0\n",
              "2023-02-23       0.217316  0.473684      3.0\n",
              "2023-02-24       0.151694  0.448980      4.0\n",
              "2023-02-25            NaN       NaN      NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ecbb6c6d-91e5-4107-b4f6-1c81c8a77245\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Duration (hr)</th>\n",
              "      <th>Value</th>\n",
              "      <th>weekday</th>\n",
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              "    <tr>\n",
              "      <th>Sl_start</th>\n",
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              "      <th>2023-02-21</th>\n",
              "      <td>0.222480</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>2023-02-22</th>\n",
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              "      <td>NaN</td>\n",
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              "      </button>\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ecbb6c6d-91e5-4107-b4f6-1c81c8a77245 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ecbb6c6d-91e5-4107-b4f6-1c81c8a77245');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tP9grzkC-UfP"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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      },
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    }
  ]
}