시계열.ipynb

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      "authorship_tag": "ABX9TyPcWaKD2iFTVUSF7VfssU2e",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/heejjj/Sleeping_Project/blob/main/%EC%8B%9C%EA%B3%84%EC%97%B4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Applewatch data 불러오기"
      ],
      "metadata": {
        "id": "Q5MF7DkX0r7P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-N1wWDdQ0dOE",
        "outputId": "3d315793-92fd-48e7-c106-ca8f1ef13377"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv('/content/drive/MyDrive/KDT_python/project/Hackathon/Sleep Analysis Data (1).csv')"
      ],
      "metadata": {
        "id": "aMk-NV4k0dwJ"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "1kQrQRfy0dK0",
        "outputId": "27e8c499-80bc-45dc-e83d-634f879db97a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2955, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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          "height": 206
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        "id": "lX3w5S0k0naR",
        "outputId": "055ad89c-8ffb-4766-b2a3-cbe7dd8a2001"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
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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",
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              "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": "code",
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        "data.tail()"
      ],
      "metadata": {
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        "id": "95umGg4M1405",
        "outputId": "c5305356-a099-460e-dcd8-70b3a1410851"
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      "execution_count": 17,
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        {
          "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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              "                                                     [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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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
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        "colab": {
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        },
        "id": "hGhrQgZx2GPg",
        "outputId": "9fdd10a6-0823-4b63-dd47-f815e5130ff1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2955 entries, 0 to 2954\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Start          2955 non-null   object \n",
            " 1   End            2955 non-null   object \n",
            " 2   Duration (hr)  2955 non-null   float64\n",
            " 3   Value          2955 non-null   object \n",
            " 4   Source         2955 non-null   object \n",
            "dtypes: float64(1), object(4)\n",
            "memory usage: 115.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 저장.\n",
        "sleep = data"
      ],
      "metadata": {
        "id": "0QQq6QPS2HZD"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA"
      ],
      "metadata": {
        "id": "x1YtbfTM1-sR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- \"data\"는 2023.02.13 저녁부터 2023.05.03까지의 수면시간 데이터가 저장되어 있음.\n",
        "\n",
        "- Start\n",
        "- End\n",
        "- Duration (hr)\n",
        "- Value : 수면 깊이. deep, core, rem, awake\n",
        "- Source : nhc(휴대폰), Sujeong의 Apple watch(워치)"
      ],
      "metadata": {
        "id": "t2X56C_K2A27"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- ['Source' == 'nhc'] 의 'Value'가 'InBed'인 경우는 한 번 수면한 총 시간 정보를 가지고 있으므로 따로 저장하여 도탈 시간과 비교하여 데이터가 정교한지(?)를 확인.\n",
        "- ['Source' == 'Sujeong의 Apple watch'] 의 'Value'가 'InBed'인 경우는 이상치로 구분하여 drop 하기로 결정.\n",
        "\n",
        "- 분석 구간 (2/17, 2/25, 3/20, 3/26, 4/22 에 워치를 착용하지 않고 수면함. 이상치 -> 제거 결정, 규칙적인 수면이 시작되는 2/26일부터 분석 시작)\n",
        "  - first - 2/26-3/25 (2/26-3/20, 3/22-3/25)\n",
        "  - second- 3/27~4/21\n",
        "  - third - 4/23~5/3 \n",
        "\n",
        "- deep: 1, core:2, rem:3, awake:4"
      ],
      "metadata": {
        "id": "Tepk5Lkb0rQo"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vWkj22WH4GZi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# total 수면 시간 비교를 위한 \"nhc\" 데이터셋 생성\n",
        "InBed = data[data[\"Value\"]=='InBed']\n",
        "nhc = InBed[InBed[\"Source\"] == 'NHC']\n",
        "\n",
        "# 워치 데이터에서 InBed를 제외한 데이터인 \"watch\" 데이터셋 생성.\n",
        "watch = data[data[\"Source\"] != 'NHC']\n",
        "watch = watch[watch['Value'] != \"InBed\"]"
      ],
      "metadata": {
        "id": "PeQH7wLf3G9M"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nhc.head()"
      ],
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        "colab": {
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        "id": "mQP2dqRR4aLn",
        "outputId": "3c7fe7b1-cf86-4c99-b4b3-f0de7df5ac54"
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      "execution_count": 32,
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            "text/plain": [
              "                   Start                  End  Duration (hr)  Value Source\n",
              "0    2023-02-13 23:21:07  2023-02-14 08:47:48           9.44  InBed    NHC\n",
              "36   2023-02-14 23:16:52  2023-02-15 09:00:24           9.73  InBed    NHC\n",
              "93   2023-02-15 23:50:07  2023-02-16 06:36:44           6.78  InBed    NHC\n",
              "138  2023-02-16 06:36:50  2023-02-16 09:00:17           2.39  InBed    NHC\n",
              "160  2023-02-17 00:40:33  2023-02-17 09:00:08           8.33  InBed    NHC"
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              "      <td>8.33</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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        "watch.head()"
      ],
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          "height": 206
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        "id": "jODP48g84cQk",
        "outputId": "236d497c-6209-4176-a7e0-75240953a52c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Start                  End  Duration (hr) Value  \\\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",
              "5  2023-02-14 00:38:25  2023-02-14 00:39:55          0.025  Core   \n",
              "\n",
              "                 Source  \n",
              "1  Sujeong의 Apple Watch  \n",
              "2  Sujeong의 Apple Watch  \n",
              "3  Sujeong의 Apple Watch  \n",
              "4  Sujeong의 Apple Watch  \n",
              "5  Sujeong의 Apple Watch  "
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              "                                                     [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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              "  "
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          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# \"Source\" 열의 데이터를 레이블링 해줌.\n",
        "# deep: 1, core:2, rem:3, awake:4\n",
        "watch['Value']= watch['Value'].replace('Deep',1)\n",
        "watch['Value']= watch['Value'].replace('Core',2)\n",
        "watch['Value']= watch['Value'].replace('REM',3)\n",
        "watch['Value']= watch['Value'].replace('Awake',4)"
      ],
      "metadata": {
        "id": "ruaVbQS4161b"
      },
      "execution_count": 47,
      "outputs": []
    },
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        "watch.head()"
      ],
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        },
        "id": "JsXoTID56oRF",
        "outputId": "755ab28a-1438-4c3d-cd99-345fa148a967"
      },
      "execution_count": 48,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                 Start                  End  Duration (hr)  Value  \\\n",
              "1  2023-02-13 23:50:25  2023-02-14 00:07:25          0.283      2   \n",
              "2  2023-02-14 00:07:25  2023-02-14 00:27:25          0.333      1   \n",
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              "\n",
              "                 Source  \n",
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              "3  Sujeong의 Apple Watch  \n",
              "4  Sujeong의 Apple Watch  \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",
              "      <td>0.283</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-02-14 00:07:25</td>\n",
              "      <td>2023-02-14 00:27:25</td>\n",
              "      <td>0.333</td>\n",
              "      <td>1</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-02-14 00:27:25</td>\n",
              "      <td>2023-02-14 00:30:25</td>\n",
              "      <td>0.050</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "      <td>1</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2023-02-14 00:38:25</td>\n",
              "      <td>2023-02-14 00:39:55</td>\n",
              "      <td>0.025</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c06e7612-1cc8-4298-b5d3-852250eee7e8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 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",
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              "\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",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-c06e7612-1cc8-4298-b5d3-852250eee7e8 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-c06e7612-1cc8-4298-b5d3-852250eee7e8');\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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Gf2_DRIu6pyf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Series to datetime\n",
        "# 데이터 타입을 datetime으로 변경. 날짜와 시간 추출이 가능하도록 함.\n",
        "watch['Start'] = pd.to_datetime(watch['Start'], format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "watch['End'] = pd.to_datetime(watch['End'], format='%Y-%m-%d %H:%M:%S', errors='raise')"
      ],
      "metadata": {
        "id": "qD4c4Odm4eC5"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "watch.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5We5T0614GWN",
        "outputId": "438da5c2-eacb-418c-dcf1-0869c066e98e"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2267 entries, 1 to 2953\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   Start          2267 non-null   datetime64[ns]\n",
            " 1   End            2267 non-null   datetime64[ns]\n",
            " 2   Duration (hr)  2267 non-null   float64       \n",
            " 3   Value          2267 non-null   int64         \n",
            " 4   Source         2267 non-null   object        \n",
            "dtypes: datetime64[ns](2), float64(1), int64(1), object(1)\n",
            "memory usage: 106.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 예를 들어, 2월 26일 수면 시간을 보고싶다고 했을 때 2월 25일 11시에 수면을 시작했다면 해당 정보가 누락될 위험이 있으므로 항상 날짜를 가져올 때는(2/26 - 3/20) 구간의 시작 전날 12:00:00 부터(2023-02-25 12:00:00) 구간의 마지막날 12:00:00 까지(2023-03-20 12:00:00) 가져와야 함을 주의."
      ],
      "metadata": {
        "id": "5jkuR6bj7B6A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # 하나의 datetime에 대한 정보 추출은 다음과 같이 할 수 있음\n",
        "# watch.iloc[0,0].year\n",
        "# watch.iloc[0,0].month\n",
        "# watch.iloc[0,0].day\n",
        "\n",
        "# # 전체 dt에 대한 datetime 정보를 추출하려면,\n",
        "# watch['Start'].dt.date\n",
        "# watch['day'] = watch['Start'].dt.day \n",
        "# watch['month'] =watch['Start'].dt.month\n",
        "# watch['year'] = watch['Start'].dt.year"
      ],
      "metadata": {
        "id": "VQl4ATeA2W1T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# first - 2/26-3/25 (2/26-3/20, 3/22-3/25)\n",
        "start_date = '2023-02-25 12:00:00'\n",
        "end_date = '2023-03-20 12:00:00'\n",
        "first = watch[(watch['Start']>=start_date) & (watch['Start']<=end_date)]"
      ],
      "metadata": {
        "id": "DYt2n_xV6ztg"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "RyR4o6137kWP",
        "outputId": "5b4fb998-67a9-4d49-b09d-6b61a3a170a5"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Start                 End  Duration (hr)  Value  \\\n",
              "495  2023-02-26 00:00:40 2023-02-26 00:03:40          0.050      2   \n",
              "497  2023-02-26 00:03:40 2023-02-26 00:09:40          0.100      4   \n",
              "498  2023-02-26 00:09:40 2023-02-26 00:13:10          0.058      2   \n",
              "500  2023-02-26 00:13:10 2023-02-26 00:14:40          0.025      4   \n",
              "501  2023-02-26 00:14:40 2023-02-26 00:34:10          0.325      2   \n",
              "...                  ...                 ...            ...    ...   \n",
              "1405 2023-03-20 03:42:35 2023-03-20 04:52:05          1.160      2   \n",
              "1406 2023-03-20 04:52:05 2023-03-20 05:31:05          0.650      3   \n",
              "1407 2023-03-20 05:31:05 2023-03-20 06:46:35          1.260      2   \n",
              "1408 2023-03-20 06:46:35 2023-03-20 07:23:35          0.617      3   \n",
              "1409 2023-03-20 07:23:35 2023-03-20 07:25:35          0.033      2   \n",
              "\n",
              "                    Source  \n",
              "495   Sujeong의 Apple Watch  \n",
              "497   Sujeong의 Apple Watch  \n",
              "498   Sujeong의 Apple Watch  \n",
              "500   Sujeong의 Apple Watch  \n",
              "501   Sujeong의 Apple Watch  \n",
              "...                    ...  \n",
              "1405  Sujeong의 Apple Watch  \n",
              "1406  Sujeong의 Apple Watch  \n",
              "1407  Sujeong의 Apple Watch  \n",
              "1408  Sujeong의 Apple Watch  \n",
              "1409  Sujeong의 Apple Watch  \n",
              "\n",
              "[697 rows x 5 columns]"
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              "\n",
              "  <div id=\"df-e486feb4-eeea-48b8-acd3-4d1ebb77ab51\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "  <tbody>\n",
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              "      <th>495</th>\n",
              "      <td>2023-02-26 00:00:40</td>\n",
              "      <td>2023-02-26 00:03:40</td>\n",
              "      <td>0.050</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>2023-02-26 00:03:40</td>\n",
              "      <td>2023-02-26 00:09:40</td>\n",
              "      <td>0.100</td>\n",
              "      <td>4</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>2023-02-26 00:09:40</td>\n",
              "      <td>2023-02-26 00:13:10</td>\n",
              "      <td>0.058</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>2023-02-26 00:13:10</td>\n",
              "      <td>2023-02-26 00:14:40</td>\n",
              "      <td>0.025</td>\n",
              "      <td>4</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>2023-02-26 00:14:40</td>\n",
              "      <td>2023-02-26 00:34:10</td>\n",
              "      <td>0.325</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1405</th>\n",
              "      <td>2023-03-20 03:42:35</td>\n",
              "      <td>2023-03-20 04:52:05</td>\n",
              "      <td>1.160</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1406</th>\n",
              "      <td>2023-03-20 04:52:05</td>\n",
              "      <td>2023-03-20 05:31:05</td>\n",
              "      <td>0.650</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1407</th>\n",
              "      <td>2023-03-20 05:31:05</td>\n",
              "      <td>2023-03-20 06:46:35</td>\n",
              "      <td>1.260</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1408</th>\n",
              "      <td>2023-03-20 06:46:35</td>\n",
              "      <td>2023-03-20 07:23:35</td>\n",
              "      <td>0.617</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1409</th>\n",
              "      <td>2023-03-20 07:23:35</td>\n",
              "      <td>2023-03-20 07:25:35</td>\n",
              "      <td>0.033</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>697 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e486feb4-eeea-48b8-acd3-4d1ebb77ab51')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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-e486feb4-eeea-48b8-acd3-4d1ebb77ab51 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-e486feb4-eeea-48b8-acd3-4d1ebb77ab51');\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": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# date와 time 열 추가\n",
        "first['date'] = first['Start'].dt.date\n",
        "first['time'] = first['Start'].dt.time\n",
        "\n",
        "first.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "aDp7uaFt7yH4",
        "outputId": "2236f030-771c-4e40-dae5-7d4c2889d60d"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-60-77b4cdd3e540>:2: 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",
            "  first['date'] = first['Start'].dt.date\n",
            "<ipython-input-60-77b4cdd3e540>: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",
            "  first['time'] = first['Start'].dt.time\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Start                 End  Duration (hr)  Value  \\\n",
              "495 2023-02-26 00:00:40 2023-02-26 00:03:40          0.050      2   \n",
              "497 2023-02-26 00:03:40 2023-02-26 00:09:40          0.100      4   \n",
              "498 2023-02-26 00:09:40 2023-02-26 00:13:10          0.058      2   \n",
              "500 2023-02-26 00:13:10 2023-02-26 00:14:40          0.025      4   \n",
              "501 2023-02-26 00:14:40 2023-02-26 00:34:10          0.325      2   \n",
              "\n",
              "                   Source        date      time  \n",
              "495  Sujeong의 Apple Watch  2023-02-26  00:00:40  \n",
              "497  Sujeong의 Apple Watch  2023-02-26  00:03:40  \n",
              "498  Sujeong의 Apple Watch  2023-02-26  00:09:40  \n",
              "500  Sujeong의 Apple Watch  2023-02-26  00:13:10  \n",
              "501  Sujeong의 Apple Watch  2023-02-26  00:14:40  "
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              "\n",
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              "      <td>2023-02-26 00:00:40</td>\n",
              "      <td>2023-02-26 00:03:40</td>\n",
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              "      <td>2023-02-26 00:03:40</td>\n",
              "      <td>2023-02-26 00:09:40</td>\n",
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              "      <td>2023-02-26</td>\n",
              "      <td>00:03:40</td>\n",
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              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>2023-02-26 00:09:40</td>\n",
              "      <td>2023-02-26 00:13:10</td>\n",
              "      <td>0.058</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "      <td>2023-02-26</td>\n",
              "      <td>00:13:10</td>\n",
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              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>2023-02-26 00:14:40</td>\n",
              "      <td>2023-02-26 00:34:10</td>\n",
              "      <td>0.325</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-02-26</td>\n",
              "      <td>00:14:40</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c1572094-5f02-4b69-85be-7e581d109364');\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": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 24:00로 되어 있는 값은 python datetime에서는 읽히지 않으므로 00:00으로 변경\n",
        "first['time'].replace('24:00:00','00:00:00', inplace=True)\n",
        "\n",
        "first['time'] = pd.to_datetime(first['time'], format='%H:%M:%S', errors='raise').dt.time\n",
        "\n",
        "first.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PeLHQ-z--pj7",
        "outputId": "2c0cc278-2db3-4661-e1ec-c2b7a7bb6a6c"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 697 entries, 495 to 1409\n",
            "Data columns (total 7 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   Start          697 non-null    datetime64[ns]\n",
            " 1   End            697 non-null    datetime64[ns]\n",
            " 2   Duration (hr)  697 non-null    float64       \n",
            " 3   Value          697 non-null    int64         \n",
            " 4   Source         697 non-null    object        \n",
            " 5   date           697 non-null    object        \n",
            " 6   time           697 non-null    object        \n",
            "dtypes: datetime64[ns](2), float64(1), int64(1), object(3)\n",
            "memory usage: 43.6+ KB\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-61-ee360be50a14>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\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",
            "  first['time'].replace('24:00:00','00:00:00', inplace=True)\n",
            "<ipython-input-61-ee360be50a14>: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",
            "  first['time'] = pd.to_datetime(first['time'], format='%H:%M:%S', errors='raise').dt.time\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "first.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1TR0mifYAI8X",
        "outputId": "9ade78ef-884d-4ba1-c05d-2e2a18bff96a"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Start                 End  Duration (hr)  Value  \\\n",
              "495 2023-02-26 00:00:40 2023-02-26 00:03:40          0.050      2   \n",
              "497 2023-02-26 00:03:40 2023-02-26 00:09:40          0.100      4   \n",
              "498 2023-02-26 00:09:40 2023-02-26 00:13:10          0.058      2   \n",
              "500 2023-02-26 00:13:10 2023-02-26 00:14:40          0.025      4   \n",
              "501 2023-02-26 00:14:40 2023-02-26 00:34:10          0.325      2   \n",
              "\n",
              "                   Source        date      time  \n",
              "495  Sujeong의 Apple Watch  2023-02-26  00:00:40  \n",
              "497  Sujeong의 Apple Watch  2023-02-26  00:03:40  \n",
              "498  Sujeong의 Apple Watch  2023-02-26  00:09:40  \n",
              "500  Sujeong의 Apple Watch  2023-02-26  00:13:10  \n",
              "501  Sujeong의 Apple Watch  2023-02-26  00:14:40  "
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              "      <td>2023-02-26 00:03:40</td>\n",
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              "      <td>2023-02-26 00:09:40</td>\n",
              "      <td>2023-02-26 00:13:10</td>\n",
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              "      <td>2023-02-26</td>\n",
              "      <td>00:13:10</td>\n",
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              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>2023-02-26 00:14:40</td>\n",
              "      <td>2023-02-26 00:34:10</td>\n",
              "      <td>0.325</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023-02-26</td>\n",
              "      <td>00:14:40</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "      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-5bf84398-2bae-4af1-ae58-bf61c11b2137 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-5bf84398-2bae-4af1-ae58-bf61c11b2137');\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": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NKmCsqmT-Ifs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_date = '2023-02-27 12:00:00'\n",
        "end_date = '2023-02-28 12:00:00'\n",
        "test = watch[(watch['Start']>=start_date) & (watch['Start']<=end_date)]"
      ],
      "metadata": {
        "id": "Prf5Akw3BBhx"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2/27일 저녁 -> 2/28일 수면 데이터\n",
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        },
        "id": "zRCOp5BrBPT2",
        "outputId": "0ceb1c13-e21b-40d4-c0a0-2a50ff4e0f37"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Start                 End  Duration (hr)  Value  \\\n",
              "575 2023-02-27 23:59:19 2023-02-28 00:22:49          0.392      2   \n",
              "577 2023-02-28 00:22:49 2023-02-28 00:59:19          0.608      1   \n",
              "578 2023-02-28 00:59:19 2023-02-28 01:06:19          0.117      2   \n",
              "579 2023-02-28 01:06:19 2023-02-28 01:21:19          0.250      3   \n",
              "580 2023-02-28 01:21:19 2023-02-28 02:04:19          0.717      2   \n",
              "581 2023-02-28 02:04:19 2023-02-28 02:24:19          0.333      1   \n",
              "582 2023-02-28 02:24:19 2023-02-28 03:30:19          1.100      2   \n",
              "583 2023-02-28 03:30:19 2023-02-28 03:54:49          0.408      3   \n",
              "584 2023-02-28 03:54:49 2023-02-28 03:57:49          0.050      2   \n",
              "585 2023-02-28 03:57:49 2023-02-28 04:05:19          0.125      3   \n",
              "586 2023-02-28 04:05:19 2023-02-28 04:07:19          0.033      2   \n",
              "587 2023-02-28 04:07:19 2023-02-28 04:29:19          0.367      3   \n",
              "588 2023-02-28 04:29:19 2023-02-28 05:40:49          1.190      2   \n",
              "589 2023-02-28 05:40:49 2023-02-28 05:43:19          0.042      4   \n",
              "590 2023-02-28 05:43:19 2023-02-28 05:45:19          0.033      2   \n",
              "592 2023-02-28 05:45:19 2023-02-28 05:45:49          0.008      4   \n",
              "593 2023-02-28 05:45:49 2023-02-28 05:54:49          0.150      2   \n",
              "595 2023-02-28 05:54:49 2023-02-28 05:56:49          0.033      3   \n",
              "596 2023-02-28 05:56:49 2023-02-28 05:58:19          0.025      2   \n",
              "597 2023-02-28 05:58:19 2023-02-28 06:00:49          0.042      3   \n",
              "598 2023-02-28 06:00:49 2023-02-28 06:02:49          0.033      2   \n",
              "599 2023-02-28 06:02:49 2023-02-28 06:07:49          0.083      3   \n",
              "600 2023-02-28 06:07:49 2023-02-28 06:11:19          0.058      4   \n",
              "601 2023-02-28 06:11:19 2023-02-28 06:55:19          0.733      2   \n",
              "603 2023-02-28 06:55:19 2023-02-28 06:57:19          0.033      4   \n",
              "604 2023-02-28 06:57:19 2023-02-28 06:58:19          0.017      2   \n",
              "\n",
              "                   Source  \n",
              "575  Sujeong의 Apple Watch  \n",
              "577  Sujeong의 Apple Watch  \n",
              "578  Sujeong의 Apple Watch  \n",
              "579  Sujeong의 Apple Watch  \n",
              "580  Sujeong의 Apple Watch  \n",
              "581  Sujeong의 Apple Watch  \n",
              "582  Sujeong의 Apple Watch  \n",
              "583  Sujeong의 Apple Watch  \n",
              "584  Sujeong의 Apple Watch  \n",
              "585  Sujeong의 Apple Watch  \n",
              "586  Sujeong의 Apple Watch  \n",
              "587  Sujeong의 Apple Watch  \n",
              "588  Sujeong의 Apple Watch  \n",
              "589  Sujeong의 Apple Watch  \n",
              "590  Sujeong의 Apple Watch  \n",
              "592  Sujeong의 Apple Watch  \n",
              "593  Sujeong의 Apple Watch  \n",
              "595  Sujeong의 Apple Watch  \n",
              "596  Sujeong의 Apple Watch  \n",
              "597  Sujeong의 Apple Watch  \n",
              "598  Sujeong의 Apple Watch  \n",
              "599  Sujeong의 Apple Watch  \n",
              "600  Sujeong의 Apple Watch  \n",
              "601  Sujeong의 Apple Watch  \n",
              "603  Sujeong의 Apple Watch  \n",
              "604  Sujeong의 Apple Watch  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Start</th>\n",
              "      <th>End</th>\n",
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              "      <th>575</th>\n",
              "      <td>2023-02-27 23:59:19</td>\n",
              "      <td>2023-02-28 00:22:49</td>\n",
              "      <td>0.392</td>\n",
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              "      <th>577</th>\n",
              "      <td>2023-02-28 00:22:49</td>\n",
              "      <td>2023-02-28 00:59:19</td>\n",
              "      <td>0.608</td>\n",
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              "      <td>Sujeong의 Apple Watch</td>\n",
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              "      <th>578</th>\n",
              "      <td>2023-02-28 00:59:19</td>\n",
              "      <td>2023-02-28 01:06:19</td>\n",
              "      <td>0.117</td>\n",
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              "    <tr>\n",
              "      <th>579</th>\n",
              "      <td>2023-02-28 01:06:19</td>\n",
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              "      <td>0.250</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "    <tr>\n",
              "      <th>580</th>\n",
              "      <td>2023-02-28 01:21:19</td>\n",
              "      <td>2023-02-28 02:04:19</td>\n",
              "      <td>0.717</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "    <tr>\n",
              "      <th>581</th>\n",
              "      <td>2023-02-28 02:04:19</td>\n",
              "      <td>2023-02-28 02:24:19</td>\n",
              "      <td>0.333</td>\n",
              "      <td>1</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "    <tr>\n",
              "      <th>582</th>\n",
              "      <td>2023-02-28 02:24:19</td>\n",
              "      <td>2023-02-28 03:30:19</td>\n",
              "      <td>1.100</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>583</th>\n",
              "      <td>2023-02-28 03:30:19</td>\n",
              "      <td>2023-02-28 03:54:49</td>\n",
              "      <td>0.408</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>584</th>\n",
              "      <td>2023-02-28 03:54:49</td>\n",
              "      <td>2023-02-28 03:57:49</td>\n",
              "      <td>0.050</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>585</th>\n",
              "      <td>2023-02-28 03:57:49</td>\n",
              "      <td>2023-02-28 04:05:19</td>\n",
              "      <td>0.125</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>586</th>\n",
              "      <td>2023-02-28 04:05:19</td>\n",
              "      <td>2023-02-28 04:07:19</td>\n",
              "      <td>0.033</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>587</th>\n",
              "      <td>2023-02-28 04:07:19</td>\n",
              "      <td>2023-02-28 04:29:19</td>\n",
              "      <td>0.367</td>\n",
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              "      <td>Sujeong의 Apple Watch</td>\n",
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              "    <tr>\n",
              "      <th>588</th>\n",
              "      <td>2023-02-28 04:29:19</td>\n",
              "      <td>2023-02-28 05:40:49</td>\n",
              "      <td>1.190</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>589</th>\n",
              "      <td>2023-02-28 05:40:49</td>\n",
              "      <td>2023-02-28 05:43:19</td>\n",
              "      <td>0.042</td>\n",
              "      <td>4</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590</th>\n",
              "      <td>2023-02-28 05:43:19</td>\n",
              "      <td>2023-02-28 05:45:19</td>\n",
              "      <td>0.033</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "    <tr>\n",
              "      <th>592</th>\n",
              "      <td>2023-02-28 05:45:19</td>\n",
              "      <td>2023-02-28 05:45:49</td>\n",
              "      <td>0.008</td>\n",
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              "      <td>Sujeong의 Apple Watch</td>\n",
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              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>2023-02-28 05:45:49</td>\n",
              "      <td>2023-02-28 05:54:49</td>\n",
              "      <td>0.150</td>\n",
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              "      <td>Sujeong의 Apple Watch</td>\n",
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              "      <th>595</th>\n",
              "      <td>2023-02-28 05:54:49</td>\n",
              "      <td>2023-02-28 05:56:49</td>\n",
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              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>2023-02-28 05:56:49</td>\n",
              "      <td>2023-02-28 05:58:19</td>\n",
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              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>2023-02-28 05:58:19</td>\n",
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              "      <td>0.042</td>\n",
              "      <td>3</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>2023-02-28 06:00:49</td>\n",
              "      <td>2023-02-28 06:02:49</td>\n",
              "      <td>0.033</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>2023-02-28 06:02:49</td>\n",
              "      <td>2023-02-28 06:07:49</td>\n",
              "      <td>0.083</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>600</th>\n",
              "      <td>2023-02-28 06:07:49</td>\n",
              "      <td>2023-02-28 06:11:19</td>\n",
              "      <td>0.058</td>\n",
              "      <td>4</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601</th>\n",
              "      <td>2023-02-28 06:11:19</td>\n",
              "      <td>2023-02-28 06:55:19</td>\n",
              "      <td>0.733</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>603</th>\n",
              "      <td>2023-02-28 06:55:19</td>\n",
              "      <td>2023-02-28 06:57:19</td>\n",
              "      <td>0.033</td>\n",
              "      <td>4</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604</th>\n",
              "      <td>2023-02-28 06:57:19</td>\n",
              "      <td>2023-02-28 06:58:19</td>\n",
              "      <td>0.017</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee4bd45a-6a03-4c59-a7c6-2d6ca50ee81d')\"\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",
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              "      fill: #1967D2;\n",
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              "    }\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-ee4bd45a-6a03-4c59-a7c6-2d6ca50ee81d 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-ee4bd45a-6a03-4c59-a7c6-2d6ca50ee81d');\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": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test['year'] = test['Start'].dt.year\n",
        "test['month'] =test['Start'].dt.month\n",
        "test['day'] = test['Start'].dt.day \n",
        "\n",
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BX6v7rsv9o_W",
        "outputId": "3c9af179-25b9-4397-b8ef-8475241fa261"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-69-e50dc3f98b70>: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",
            "  test['year'] = test['Start'].dt.year\n",
            "<ipython-input-69-e50dc3f98b70>:2: 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",
            "  test['month'] =test['Start'].dt.month\n",
            "<ipython-input-69-e50dc3f98b70>: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",
            "  test['day'] = test['Start'].dt.day\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Start                 End  Duration (hr)  Value  \\\n",
              "575 2023-02-27 23:59:19 2023-02-28 00:22:49          0.392      2   \n",
              "577 2023-02-28 00:22:49 2023-02-28 00:59:19          0.608      1   \n",
              "578 2023-02-28 00:59:19 2023-02-28 01:06:19          0.117      2   \n",
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              "                   Source  year  month  day  \n",
              "575  Sujeong의 Apple Watch  2023      2   27  \n",
              "577  Sujeong의 Apple Watch  2023      2   28  \n",
              "578  Sujeong의 Apple Watch  2023      2   28  \n",
              "579  Sujeong의 Apple Watch  2023      2   28  \n",
              "580  Sujeong의 Apple Watch  2023      2   28  \n",
              "581  Sujeong의 Apple Watch  2023      2   28  \n",
              "582  Sujeong의 Apple Watch  2023      2   28  \n",
              "583  Sujeong의 Apple Watch  2023      2   28  \n",
              "584  Sujeong의 Apple Watch  2023      2   28  \n",
              "585  Sujeong의 Apple Watch  2023      2   28  \n",
              "586  Sujeong의 Apple Watch  2023      2   28  \n",
              "587  Sujeong의 Apple Watch  2023      2   28  \n",
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              "593  Sujeong의 Apple Watch  2023      2   28  \n",
              "595  Sujeong의 Apple Watch  2023      2   28  \n",
              "596  Sujeong의 Apple Watch  2023      2   28  \n",
              "597  Sujeong의 Apple Watch  2023      2   28  \n",
              "598  Sujeong의 Apple Watch  2023      2   28  \n",
              "599  Sujeong의 Apple Watch  2023      2   28  \n",
              "600  Sujeong의 Apple Watch  2023      2   28  \n",
              "601  Sujeong의 Apple Watch  2023      2   28  \n",
              "603  Sujeong의 Apple Watch  2023      2   28  \n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4LSWw8AgCNFn",
        "outputId": "9ae9edfc-7ffd-4169-e6ee-863a789d8f2c"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 26 entries, 575 to 604\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   Start          26 non-null     datetime64[ns]\n",
            " 1   End            26 non-null     datetime64[ns]\n",
            " 2   Duration (hr)  26 non-null     float64       \n",
            " 3   Value          26 non-null     int64         \n",
            " 4   Source         26 non-null     object        \n",
            " 5   year           26 non-null     int64         \n",
            " 6   month          26 non-null     int64         \n",
            " 7   day            26 non-null     int64         \n",
            "dtypes: datetime64[ns](2), float64(1), int64(4), object(1)\n",
            "memory usage: 1.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test['hour'] = test['Start'].dt.hour\n",
        "\n",
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        },
        "id": "2QuObxGzCefW",
        "outputId": "5fd802f7-d733-487f-8632-dd76a1887085"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-71-4d4108c6fa08>: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",
            "  test['hour'] = test['Start'].dt.hour\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Start                 End  Duration (hr)  Value  \\\n",
              "575 2023-02-27 23:59:19 2023-02-28 00:22:49          0.392      2   \n",
              "577 2023-02-28 00:22:49 2023-02-28 00:59:19          0.608      1   \n",
              "578 2023-02-28 00:59:19 2023-02-28 01:06:19          0.117      2   \n",
              "579 2023-02-28 01:06:19 2023-02-28 01:21:19          0.250      3   \n",
              "580 2023-02-28 01:21:19 2023-02-28 02:04:19          0.717      2   \n",
              "581 2023-02-28 02:04:19 2023-02-28 02:24:19          0.333      1   \n",
              "582 2023-02-28 02:24:19 2023-02-28 03:30:19          1.100      2   \n",
              "583 2023-02-28 03:30:19 2023-02-28 03:54:49          0.408      3   \n",
              "584 2023-02-28 03:54:49 2023-02-28 03:57:49          0.050      2   \n",
              "585 2023-02-28 03:57:49 2023-02-28 04:05:19          0.125      3   \n",
              "586 2023-02-28 04:05:19 2023-02-28 04:07:19          0.033      2   \n",
              "587 2023-02-28 04:07:19 2023-02-28 04:29:19          0.367      3   \n",
              "588 2023-02-28 04:29:19 2023-02-28 05:40:49          1.190      2   \n",
              "589 2023-02-28 05:40:49 2023-02-28 05:43:19          0.042      4   \n",
              "590 2023-02-28 05:43:19 2023-02-28 05:45:19          0.033      2   \n",
              "592 2023-02-28 05:45:19 2023-02-28 05:45:49          0.008      4   \n",
              "593 2023-02-28 05:45:49 2023-02-28 05:54:49          0.150      2   \n",
              "595 2023-02-28 05:54:49 2023-02-28 05:56:49          0.033      3   \n",
              "596 2023-02-28 05:56:49 2023-02-28 05:58:19          0.025      2   \n",
              "597 2023-02-28 05:58:19 2023-02-28 06:00:49          0.042      3   \n",
              "598 2023-02-28 06:00:49 2023-02-28 06:02:49          0.033      2   \n",
              "599 2023-02-28 06:02:49 2023-02-28 06:07:49          0.083      3   \n",
              "600 2023-02-28 06:07:49 2023-02-28 06:11:19          0.058      4   \n",
              "601 2023-02-28 06:11:19 2023-02-28 06:55:19          0.733      2   \n",
              "603 2023-02-28 06:55:19 2023-02-28 06:57:19          0.033      4   \n",
              "604 2023-02-28 06:57:19 2023-02-28 06:58:19          0.017      2   \n",
              "\n",
              "                   Source  year  month  day  hour  \n",
              "575  Sujeong의 Apple Watch  2023      2   27    23  \n",
              "577  Sujeong의 Apple Watch  2023      2   28     0  \n",
              "578  Sujeong의 Apple Watch  2023      2   28     0  \n",
              "579  Sujeong의 Apple Watch  2023      2   28     1  \n",
              "580  Sujeong의 Apple Watch  2023      2   28     1  \n",
              "581  Sujeong의 Apple Watch  2023      2   28     2  \n",
              "582  Sujeong의 Apple Watch  2023      2   28     2  \n",
              "583  Sujeong의 Apple Watch  2023      2   28     3  \n",
              "584  Sujeong의 Apple Watch  2023      2   28     3  \n",
              "585  Sujeong의 Apple Watch  2023      2   28     3  \n",
              "586  Sujeong의 Apple Watch  2023      2   28     4  \n",
              "587  Sujeong의 Apple Watch  2023      2   28     4  \n",
              "588  Sujeong의 Apple Watch  2023      2   28     4  \n",
              "589  Sujeong의 Apple Watch  2023      2   28     5  \n",
              "590  Sujeong의 Apple Watch  2023      2   28     5  \n",
              "592  Sujeong의 Apple Watch  2023      2   28     5  \n",
              "593  Sujeong의 Apple Watch  2023      2   28     5  \n",
              "595  Sujeong의 Apple Watch  2023      2   28     5  \n",
              "596  Sujeong의 Apple Watch  2023      2   28     5  \n",
              "597  Sujeong의 Apple Watch  2023      2   28     5  \n",
              "598  Sujeong의 Apple Watch  2023      2   28     6  \n",
              "599  Sujeong의 Apple Watch  2023      2   28     6  \n",
              "600  Sujeong의 Apple Watch  2023      2   28     6  \n",
              "601  Sujeong의 Apple Watch  2023      2   28     6  \n",
              "603  Sujeong의 Apple Watch  2023      2   28     6  \n",
              "604  Sujeong의 Apple Watch  2023      2   28     6  "
            ],
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              "      <th></th>\n",
              "      <th>Start</th>\n",
              "      <th>End</th>\n",
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              "      <th>hour</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>575</th>\n",
              "      <td>2023-02-27 23:59:19</td>\n",
              "      <td>2023-02-28 00:22:49</td>\n",
              "      <td>0.392</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "      <th>577</th>\n",
              "      <td>2023-02-28 00:22:49</td>\n",
              "      <td>2023-02-28 00:59:19</td>\n",
              "      <td>0.608</td>\n",
              "      <td>1</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
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              "    <tr>\n",
              "      <th>578</th>\n",
              "      <td>2023-02-28 00:59:19</td>\n",
              "      <td>2023-02-28 01:06:19</td>\n",
              "      <td>0.117</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>579</th>\n",
              "      <td>2023-02-28 01:06:19</td>\n",
              "      <td>2023-02-28 01:21:19</td>\n",
              "      <td>0.250</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>580</th>\n",
              "      <td>2023-02-28 01:21:19</td>\n",
              "      <td>2023-02-28 02:04:19</td>\n",
              "      <td>0.717</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>581</th>\n",
              "      <td>2023-02-28 02:04:19</td>\n",
              "      <td>2023-02-28 02:24:19</td>\n",
              "      <td>0.333</td>\n",
              "      <td>1</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>582</th>\n",
              "      <td>2023-02-28 02:24:19</td>\n",
              "      <td>2023-02-28 03:30:19</td>\n",
              "      <td>1.100</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>583</th>\n",
              "      <td>2023-02-28 03:30:19</td>\n",
              "      <td>2023-02-28 03:54:49</td>\n",
              "      <td>0.408</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>584</th>\n",
              "      <td>2023-02-28 03:54:49</td>\n",
              "      <td>2023-02-28 03:57:49</td>\n",
              "      <td>0.050</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>585</th>\n",
              "      <td>2023-02-28 03:57:49</td>\n",
              "      <td>2023-02-28 04:05:19</td>\n",
              "      <td>0.125</td>\n",
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              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>586</th>\n",
              "      <td>2023-02-28 04:05:19</td>\n",
              "      <td>2023-02-28 04:07:19</td>\n",
              "      <td>0.033</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "    <tr>\n",
              "      <th>587</th>\n",
              "      <td>2023-02-28 04:07:19</td>\n",
              "      <td>2023-02-28 04:29:19</td>\n",
              "      <td>0.367</td>\n",
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              "    <tr>\n",
              "      <th>589</th>\n",
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              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "      <td>5</td>\n",
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              "      <th>592</th>\n",
              "      <td>2023-02-28 05:45:19</td>\n",
              "      <td>2023-02-28 05:45:49</td>\n",
              "      <td>0.008</td>\n",
              "      <td>4</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "      <td>28</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>2023-02-28 05:45:49</td>\n",
              "      <td>2023-02-28 05:54:49</td>\n",
              "      <td>0.150</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "      <td>2</td>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>2023-02-28 05:54:49</td>\n",
              "      <td>2023-02-28 05:56:49</td>\n",
              "      <td>0.033</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
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              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>2023-02-28 05:56:49</td>\n",
              "      <td>2023-02-28 05:58:19</td>\n",
              "      <td>0.025</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>2023-02-28 05:58:19</td>\n",
              "      <td>2023-02-28 06:00:49</td>\n",
              "      <td>0.042</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>2023-02-28 06:00:49</td>\n",
              "      <td>2023-02-28 06:02:49</td>\n",
              "      <td>0.033</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>2023-02-28 06:02:49</td>\n",
              "      <td>2023-02-28 06:07:49</td>\n",
              "      <td>0.083</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>600</th>\n",
              "      <td>2023-02-28 06:07:49</td>\n",
              "      <td>2023-02-28 06:11:19</td>\n",
              "      <td>0.058</td>\n",
              "      <td>4</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601</th>\n",
              "      <td>2023-02-28 06:11:19</td>\n",
              "      <td>2023-02-28 06:55:19</td>\n",
              "      <td>0.733</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>603</th>\n",
              "      <td>2023-02-28 06:55:19</td>\n",
              "      <td>2023-02-28 06:57:19</td>\n",
              "      <td>0.033</td>\n",
              "      <td>4</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604</th>\n",
              "      <td>2023-02-28 06:57:19</td>\n",
              "      <td>2023-02-28 06:58:19</td>\n",
              "      <td>0.017</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35f82894-0ba6-46f8-9bb6-80804e5e9c1f')\"\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",
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          "metadata": {},
          "execution_count": 71
        }
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        "test.info()"
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        "id": "oVtAxFzgCg_T",
        "outputId": "9870aa22-a1d7-4a7e-ef23-1a199fdc1d7b"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Int64Index: 26 entries, 575 to 604\n",
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            "---  ------         --------------  -----         \n",
            " 0   Start          26 non-null     datetime64[ns]\n",
            " 1   End            26 non-null     datetime64[ns]\n",
            " 2   Duration (hr)  26 non-null     float64       \n",
            " 3   Value          26 non-null     int64         \n",
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            " 7   day            26 non-null     int64         \n",
            " 8   hour           26 non-null     int64         \n",
            "dtypes: datetime64[ns](2), float64(1), int64(5), object(1)\n",
            "memory usage: 2.0+ KB\n"
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LVHbODfPCiUX"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "for i in range(len(test)):\n",
        "  if (test.iloc[i,8] >= 12):\n",
        "    test.iloc[i,7] += 1\n",
        "\n",
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        },
        "id": "5MFQ6b-XCO4c",
        "outputId": "351d6407-9343-4e3d-f0cf-010613a818b9"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                   Source  year  month  day  hour  \n",
              "575  Sujeong의 Apple Watch  2023      2   28    23  \n",
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              "584  Sujeong의 Apple Watch  2023      2   28     3  \n",
              "585  Sujeong의 Apple Watch  2023      2   28     3  \n",
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              "592  Sujeong의 Apple Watch  2023      2   28     5  \n",
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              "596  Sujeong의 Apple Watch  2023      2   28     5  \n",
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              "598  Sujeong의 Apple Watch  2023      2   28     6  \n",
              "599  Sujeong의 Apple Watch  2023      2   28     6  \n",
              "600  Sujeong의 Apple Watch  2023      2   28     6  \n",
              "601  Sujeong의 Apple Watch  2023      2   28     6  \n",
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              "      <td>2023-02-28 05:54:49</td>\n",
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              "      <th>596</th>\n",
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              "      <td>0.042</td>\n",
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              "      <td>28</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>2023-02-28 06:00:49</td>\n",
              "      <td>2023-02-28 06:02:49</td>\n",
              "      <td>0.033</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>2023-02-28 06:02:49</td>\n",
              "      <td>2023-02-28 06:07:49</td>\n",
              "      <td>0.083</td>\n",
              "      <td>3</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>600</th>\n",
              "      <td>2023-02-28 06:07:49</td>\n",
              "      <td>2023-02-28 06:11:19</td>\n",
              "      <td>0.058</td>\n",
              "      <td>4</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601</th>\n",
              "      <td>2023-02-28 06:11:19</td>\n",
              "      <td>2023-02-28 06:55:19</td>\n",
              "      <td>0.733</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>603</th>\n",
              "      <td>2023-02-28 06:55:19</td>\n",
              "      <td>2023-02-28 06:57:19</td>\n",
              "      <td>0.033</td>\n",
              "      <td>4</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604</th>\n",
              "      <td>2023-02-28 06:57:19</td>\n",
              "      <td>2023-02-28 06:58:19</td>\n",
              "      <td>0.017</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "      <td>2023</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3aefe42-90f6-4137-a08d-e4f4e2582252')\"\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-d3aefe42-90f6-4137-a08d-e4f4e2582252 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-d3aefe42-90f6-4137-a08d-e4f4e2582252');\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": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.lineplot(data=test,x='Start',y='Value', hue='day')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "TWb2fwrTC1l9",
        "outputId": "a164d625-e36f-4164-c6d9-d666dce3bb52"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Start', ylabel='Value'>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
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      ]
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}