ML_Mini_project.ipynb

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          },
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        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "df=pd.read_csv(\"/content/merge-csv.com__65603980ba1ee (1).csv\")\n",
        "df"
      ]
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      "source": [
        "df = df['# CSV-File created with merge-csv.com'].str.split('\\t', expand=True)\n",
        "df.columns = ['Date.Time', 'Month', 'Day.Fraction', 'Indv.ID', 'Altitude']"
      ],
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        "id": "seVFJiJDfv01"
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              "                                                 Date.Time  Month  \\\n",
              "0        # --------------------------------------------...   None   \n",
              "1                                                Date.Time  Month   \n",
              "2                                      2006-10-03 07:00:00     10   \n",
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              "4                                      2006-10-06 07:00:00     10   \n",
              "...                                                    ...    ...   \n",
              "1651901                                2015-04-03 18:00:31      4   \n",
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              "1651904                                2015-04-06 18:00:56      4   \n",
              "1651905                                2015-04-07 18:20:44      4   \n",
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              "         Day.Fraction  Indv.ID  Altitude  \n",
              "0                None     None      None  \n",
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              "2                   1    65955       196  \n",
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              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-937a694e-2544-4bf3-b6ce-20c0b63d4d32 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-937a694e-2544-4bf3-b6ce-20c0b63d4d32');\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",
              "\n",
              "\n",
              "<div id=\"df-4c21f62b-f41c-4a33-9f0a-e7d31e8e9f0f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4c21f62b-f41c-4a33-9f0a-e7d31e8e9f0f')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4c21f62b-f41c-4a33-9f0a-e7d31e8e9f0f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(index=[0,1], axis = 0, inplace = True)"
      ],
      "metadata": {
        "id": "0PbAcBjPh0so"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info();\n",
        "df.columns;\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "qIJLiR1til2U",
        "outputId": "bb6163e6-9e2b-4cbe-8dbc-ea65efa5146d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1651904 entries, 2 to 1651905\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count    Dtype \n",
            "---  ------        --------------    ----- \n",
            " 0   Date.Time     1651904 non-null  object\n",
            " 1   Month         1651904 non-null  object\n",
            " 2   Day.Fraction  1651904 non-null  object\n",
            " 3   Indv.ID       1651904 non-null  object\n",
            " 4   Altitude      1651904 non-null  object\n",
            "dtypes: object(5)\n",
            "memory usage: 63.0+ MB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Date.Time    Month Day.Fraction   Indv.ID Altitude\n",
              "count               1651904  1651904      1651904   1651904  1651904\n",
              "unique               998817       12           15       211    18841\n",
              "top     2016-02-17 11:00:07        7            9  47609767        0\n",
              "freq                     27   183569       125133     31021   868284"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fd79fcf4-ace4-4f22-9353-2b5f7a10840b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date.Time</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day.Fraction</th>\n",
              "      <th>Indv.ID</th>\n",
              "      <th>Altitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1651904</td>\n",
              "      <td>1651904</td>\n",
              "      <td>1651904</td>\n",
              "      <td>1651904</td>\n",
              "      <td>1651904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>998817</td>\n",
              "      <td>12</td>\n",
              "      <td>15</td>\n",
              "      <td>211</td>\n",
              "      <td>18841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>2016-02-17 11:00:07</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>47609767</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>27</td>\n",
              "      <td>183569</td>\n",
              "      <td>125133</td>\n",
              "      <td>31021</td>\n",
              "      <td>868284</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd79fcf4-ace4-4f22-9353-2b5f7a10840b')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-fd79fcf4-ace4-4f22-9353-2b5f7a10840b 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-fd79fcf4-ace4-4f22-9353-2b5f7a10840b');\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",
              "\n",
              "\n",
              "<div id=\"df-14212a2b-50ab-48fe-a14f-7254f51fac01\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-14212a2b-50ab-48fe-a14f-7254f51fac01')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-14212a2b-50ab-48fe-a14f-7254f51fac01 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VpAnItyf0MVq",
        "outputId": "8f21335b-5b04-405e-dfa7-bb81374becf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o__9qIHyn4HK",
        "outputId": "157f7b32-1296-470e-faa4-8b47bf2e17a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date.Time       object\n",
              "Month           object\n",
              "Day.Fraction    object\n",
              "Indv.ID         object\n",
              "Altitude        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Indv.ID\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRD0xH0azEd-",
        "outputId": "655194c3-063a-47e2-a83b-8e0874f88ec8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['65955', '65956', '65957', '65958', '67252', '67253', '67254',\n",
              "       '67255', '67256', '67257', '67258', 'Carrascalinho', 'Coa',\n",
              "       'Murtigao', 'Orca', '130087972', '130088008', '130088753',\n",
              "       '130089260', '130089678', '130090821', '130090831', '130090842',\n",
              "       '130291527', '135368877', '135369082', '135369201', '135369277',\n",
              "       '135369403', '135369664', '135369771', '135370038', '135370119',\n",
              "       '135370437', '135370640', '135811990', '135815087', '135815097',\n",
              "       '135815139', '135815167', '160781143', '254508470', '46346642',\n",
              "       '46346678', '47609676', '47609689', '47609704', '47609719',\n",
              "       '47609726', '47609738', '47609745', '47609756', '47609767',\n",
              "       '47609779', '47609790', '47609801', '47609812', '47609824',\n",
              "       '47781481', '47871448', '47871481', '47871487', '47871511',\n",
              "       '47871525', '47871554', '47871564', '47871582', '47871609',\n",
              "       '47871647', '47980732', '47980749', '48070846', 'ADAN', 'ALCUDIA',\n",
              "       'APOLO', 'ARTESA', 'CHISPUN', 'COTOFLUIX', 'NOGUERA', 'PILOTO',\n",
              "       'SANTORENS', 'T1U', 'T22', 'T2N', 'T35', 'TOSSAL', 'VERRUGA',\n",
              "       'VUS01', 'VUS02', 'VUS03', 'VUS04', 'VUS05', 'VUS06', 'VUS07',\n",
              "       'VUS08', 'VUS09', 'VUS10', 'VUS11', 'VUS12', 'VWC', 'VWJ', 'VWT',\n",
              "       'VWU', 'ZZBLASCO', 'ZZGRANIZO', 'ZZPEDRA', 'ZZQUINTANA',\n",
              "       '596776617', 'Angela', 'Arredondo', 'Balou', 'Basti', 'Betty',\n",
              "       'Blacky', 'Blas', 'BlasJR', 'Bubbel', 'C64W', 'C666', 'C703',\n",
              "       'C708', 'C70V', 'C711', 'C7VH', 'C7W0', 'C7X7', 'C8WN', 'CAMF',\n",
              "       'Charlie', 'Chompy', 'Coco', 'Conchito', 'Cookie', 'Crisps',\n",
              "       'Cuqui', 'CuquiJR', 'Ekky', 'Emil', 'Eugen', 'Europa', 'Fanny',\n",
              "       'Fifi', 'Flack', 'FlackJR', 'Flower', 'Flummy', 'Frank', 'Franz',\n",
              "       'Gloria', 'Hans', 'Hansi', 'Jackie', 'Javier', 'JavierJR', 'Julia',\n",
              "       'Julian', 'Kasimir', 'Kiki', 'Kim', 'Konstantina', 'Kristall',\n",
              "       'Lewis', 'Linus-A', 'Linus-B', 'Lloyd', 'Lola', 'Louis', 'Marisa',\n",
              "       'Max', 'Mengi', 'Mia', 'Mirabell', 'MSWi', 'Muda', 'MudaJR',\n",
              "       'Muffine', 'Nano', 'Naomi', 'NaomiJR', 'Niclas', 'Olea', 'Paco',\n",
              "       'Peaches', 'Pica', 'PicaJR', 'Pipe', 'Pjotre', 'Pureta',\n",
              "       'Redrunner', 'Ronja', 'Sara', 'Schwitza', 'Sierit-chick', 'Sipp',\n",
              "       'Sol', 'Tammy', 'Tracy', 'Wendelin', 'Wendelina', 'Wibi', 'Yaya',\n",
              "       'Zlipi', 'Spiderwoman', 'Tobi', 'Amadeus', 'Ella', 'Ohnezahn',\n",
              "       'Lucky', 'Snowy', 'Toto', 'Mortimer'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Altitude\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Xs9BRexzQcl",
        "outputId": "301e86ff-587d-4771-87ce-1973e41758a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['196', '265', '52', ..., '-56.5', '1902.5', '-153.9'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming df is your DataFrame and 'Altitude' is the column you want to convert\n",
        "df['Altitude'] = pd.to_numeric(df['Altitude'], errors='coerce', downcast='integer')"
      ],
      "metadata": {
        "id": "rACwz0nHz0R2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Fit and transform the data\n",
        "IndvID = label_encoder.fit_transform(df['Indv.ID'])\n",
        "Date_Time = label_encoder.fit_transform(df['Date.Time'])\n",
        "IndvID"
      ],
      "metadata": {
        "id": "1CggKXc00Z7c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0f8a7dc-1f11-4222-c69c-64179ce5e355"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 58,  58,  58, ..., 210, 210, 210])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"IndvID\"] = IndvID\n",
        "df[\"Date_Time\"] = Date_Time"
      ],
      "metadata": {
        "id": "dxKDtbjr8yCn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.drop(columns = [\"Indv.ID\", \"Date.Time\"])"
      ],
      "metadata": {
        "id": "6qWR7UCQcbdj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "id": "l8-LWrB0froB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "2de8a4af-acc8-4211-f3b2-97ab62810e98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Month Day.Fraction  Altitude  IndvID  Date_Time\n",
              "2          10            1     196.0      58        347\n",
              "3          10            1     265.0      58        355\n",
              "4          10            1      52.0      58        359\n",
              "5          10            1     276.0      58        379\n",
              "6          12            1     290.0      58        688\n",
              "...       ...          ...       ...     ...        ...\n",
              "1651901     4           15       0.0     210     104764\n",
              "1651902     4           15       0.0     210     105271\n",
              "1651903     4           15       0.0     210     105729\n",
              "1651904     4           15       0.0     210     106209\n",
              "1651905     4           15       0.0     210     106705\n",
              "\n",
              "[1651904 rows x 5 columns]"
            ],
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              "\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-15edf401-aac8-4c1e-8959-59d375cf2e75 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_cols=['Month','IndvID']\n",
        "X=df1[feature_cols]\n",
        "y=df1.Altitude\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "Cpw9pHkJkZ_j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.preprocessing import KBinsDiscretizer\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming you have a dataset (X, y) where X is your feature matrix and y is your continuous target variable\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Discretize the continuous target variable into bins\n",
        "k_bins_discretizer = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform')\n",
        "y_train_discrete = k_bins_discretizer.fit_transform(y_train.values.reshape(-1, 1))\n",
        "y_test_discrete = k_bins_discretizer.transform(y_test.values.reshape(-1, 1))\n",
        "\n",
        "# Create a Random Forest Classifier\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Train the classifier on the training data\n",
        "rf_classifier.fit(X_train, y_train_discrete)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred_discrete = rf_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the performance of the classifier\n",
        "accuracy = accuracy_score(y_test_discrete, y_pred_discrete)\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "# You can also print other metrics like classification report\n",
        "print(\"Classification Report:\\n\", classification_report(y_test_discrete, y_pred_discrete))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rU-zgD3tkMk5",
        "outputId": "f3facb2b-8502-4d9e-f566-cf36f15a1639"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-1f0205838af5>:21: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  rf_classifier.fit(X_train, y_train_discrete)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.99\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.00      0.00      0.00       106\n",
            "         1.0       0.99      1.00      0.99    326916\n",
            "         2.0       0.00      0.00      0.00      3359\n",
            "\n",
            "    accuracy                           0.99    330381\n",
            "   macro avg       0.33      0.33      0.33    330381\n",
            "weighted avg       0.98      0.99      0.98    330381\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "# Assuming you have a dataset (X, y) where X is your feature matrix and y is your continuous target variable\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create a Gradient Boosting Regressor\n",
        "gb_regressor = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42)\n",
        "\n",
        "# Train the regressor on the training data\n",
        "gb_regressor.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred = gb_regressor.predict(X_test)\n",
        "\n",
        "# Evaluate the performance of the regressor\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(f\"Mean Squared Error: {mse:.2f}\")\n",
        "\n",
        "accuracy = accuracy_score(y_test_discrete, y_pred_discrete)\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n"
      ],
      "metadata": {
        "id": "iQnKPgkdq9wk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "266f5a8f-c3a3-4819-ce4b-773bd629d51a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 45226.72\n",
            "Accuracy: 0.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "# Assuming you have a dataset (X, y) where X is your feature matrix and y is your continuous target variable\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create a Support Vector Regressor\n",
        "svr_regressor = SVR(kernel='linear')  # You can choose different kernels like 'linear', 'rbf', etc.\n",
        "\n",
        "# Train the regressor on the training data\n",
        "svr_regressor.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred = svr_regressor.predict(X_test)\n",
        "\n",
        "# Evaluate the performance of the regressor\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(f\"Mean Squared Error: {mse:.2f}\")"
      ],
      "metadata": {
        "id": "dcDv2doMTEb7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming clf is your trained Random Forest classifier\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "feature_importances = rf_classifier.feature_importances_\n",
        "feature_names = feature_cols # Replace with your actual feature names\n",
        "\n",
        "# Sort features by importance\n",
        "sorted_idx = feature_importances.argsort()\n",
        "\n",
        "plt.barh(range(len(feature_importances)), feature_importances[sorted_idx])\n",
        "plt.yticks(range(len(feature_importances)), [feature_names[i] for i in sorted_idx])\n",
        "plt.xlabel('Random Forest Feature Importance')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "LuzGzAQ0MQDT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "00c5195a-0661-4130-f5cc-cea07f4a47d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assume df is a DataFrame with a time column and a target variable\n",
        "# Adjust this based on your actual dataset\n",
        "\n",
        "\n",
        "# Feature engineering (add more features as needed)\n",
        "df['day_of_week'] = df['Date.Time'].dt.dayofweek\n",
        "df['month'] = df['Date.Time'].dt.month\n",
        "df['day_of_year'] = df['Date.Time'].dt.dayofyear\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "train_size = int(len(df) * 0.8)\n",
        "train, test = df[:train_size], df[train_size:]\n",
        "\n",
        "# Define features and target variable\n",
        "features = ['day_of_week', 'month', 'day_of_year']\n",
        "target = df['Altitude']\n",
        "\n",
        "X_train, y_train = train[feature_cols], train[target]\n",
        "X_test, y_test = test[features_cols], test[target]\n",
        "\n",
        "# Convert data to DMatrix format for XGBoost\n",
        "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
        "dtest = xgb.DMatrix(X_test, label=y_test)\n",
        "\n",
        "# Define XGBoost parameters\n",
        "params = {\n",
        "    'objective': 'reg:squarederror',  # Regression task\n",
        "    'eval_metric': 'rmse',  # Root Mean Squared Error\n",
        "    'max_depth': 3,\n",
        "    'learning_rate': 0.1,\n",
        "    'n_estimators': 100\n",
        "}\n",
        "\n",
        "# Train the XGBoost model\n",
        "model = xgb.train(params, dtrain, num_boost_round=params['n_estimators'])\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(dtest)\n",
        "\n",
        "# Evaluate the model\n",
        "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
        "print(f'Root Mean Squared Error (RMSE): {rmse}')\n",
        "\n",
        "# Visualize predictions vs. actual values\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(test['date'], y_test, label='Actual', marker='o')\n",
        "plt.plot(test['date'], y_pred, label='Predicted', marker='o')\n",
        "plt.title('XGBoost Time Series Forecasting')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Target Variable')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "9i-DKIN3VBjT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "d5ab89d3-f2a1-4a13-a31e-a7c2615e8e33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-434a598d1934>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Feature engineering (add more features as needed)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'day_of_week'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date.Time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdayofweek\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'month'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date.Time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'day_of_year'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date.Time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdayofyear\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5900\u001b[0m         ):\n\u001b[1;32m   5901\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5902\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5904\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/accessor.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;31m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0maccessor_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;31m# Replace the property with the accessor object. Inspired by:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;31m# https://www.pydanny.com/cached-property.html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/accessors.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data)\u001b[0m\n\u001b[1;32m    510\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mPeriodProperties\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 512\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only use .dt accessor with datetimelike values\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: Can only use .dt accessor with datetimelike values"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "kQ9W_M8Lh5a_",
        "outputId": "471e5cf7-1c4b-4eea-e977-6dcb7a5e8d87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Date.Time Month Day.Fraction Indv.ID  Altitude  IndvID  \\\n",
              "2        2006-10-03 07:00:00    10            1   65955     196.0      58   \n",
              "3        2006-10-05 07:00:00    10            1   65955     265.0      58   \n",
              "4        2006-10-06 07:00:00    10            1   65955      52.0      58   \n",
              "5        2006-10-11 07:00:00    10            1   65955     276.0      58   \n",
              "6        2006-12-27 08:00:00    12            1   65955     290.0      58   \n",
              "...                      ...   ...          ...     ...       ...     ...   \n",
              "1651901  2015-04-03 18:00:31     4           15   Zlipi       0.0     210   \n",
              "1651902  2015-04-04 18:00:15     4           15   Zlipi       0.0     210   \n",
              "1651903  2015-04-05 18:00:26     4           15   Zlipi       0.0     210   \n",
              "1651904  2015-04-06 18:00:56     4           15   Zlipi       0.0     210   \n",
              "1651905  2015-04-07 18:20:44     4           15   Zlipi       0.0     210   \n",
              "\n",
              "         Date_Time  \n",
              "2              347  \n",
              "3              355  \n",
              "4              359  \n",
              "5              379  \n",
              "6              688  \n",
              "...            ...  \n",
              "1651901     104764  \n",
              "1651902     105271  \n",
              "1651903     105729  \n",
              "1651904     106209  \n",
              "1651905     106705  \n",
              "\n",
              "[1651904 rows x 7 columns]"
            ],
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              "      <th>Date.Time</th>\n",
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              "      <th>2</th>\n",
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              "      <td>379</td>\n",
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              "      <th>6</th>\n",
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              "      <td>12</td>\n",
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              "      <td>65955</td>\n",
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              "      <th>...</th>\n",
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              "      <th>1651901</th>\n",
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              "      <td>2015-04-06 18:00:56</td>\n",
              "      <td>4</td>\n",
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              "      <td>Zlipi</td>\n",
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              "      <td>2015-04-07 18:20:44</td>\n",
              "      <td>4</td>\n",
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              "      <td>Zlipi</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d83cd73f-064e-4f1e-ac9e-8ca2fa7d8426')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-105b60ce-9cda-4786-8441-24b8e58967e8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4d4z9rC0h50L"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}