Regression_Metereological_data.ipynb

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      "name": "Regression_Metereological_data.ipynb",
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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/gabrielblins/regression-meteorological-python/blob/master/Regression_Metereological_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hBXuPPnIwYqO"
      },
      "source": [
        "##Regression project using meteorological data\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "By:Gabriel Barros Lins"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WuzpCyhikjGT"
      },
      "source": [
        "####Importing kaggle dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CiSZ2jnjV0Z"
      },
      "source": [
        "!mkdir ~/.kaggle"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MLDmVN1dwvet"
      },
      "source": [
        "Remember to upload your kaggle.json file to use kaggle API"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sw2lLdOhjeWY"
      },
      "source": [
        "!cp kaggle.json ~/.kaggle/"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Hk7OXFGjpi3"
      },
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T959mKWZjxvD",
        "outputId": "3782c420-cdb7-4463-c4a2-2aeae9ce2ffd"
      },
      "source": [
        "!kaggle datasets download -d saurabhshahane/meteorological-data-set"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading meteorological-data-set.zip to /content\n",
            " 88% 26.0M/29.6M [00:01<00:00, 20.9MB/s]\n",
            "100% 29.6M/29.6M [00:01<00:00, 26.6MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sUCQPzxzj7CT",
        "outputId": "5845b5a4-a16c-4072-d8af-76edfb9db84c"
      },
      "source": [
        "!unzip meteorological-data-set.zip && rm *.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  meteorological-data-set.zip\n",
            "  inflating: Dataset_FUERTEVENTURA.csv  \n",
            "  inflating: Dataset_GC.csv          \n",
            "  inflating: Dataset_Gomera.csv      \n",
            "  inflating: Dataset_Hierro.csv      \n",
            "  inflating: Dataset_LANZAROTE.csv   \n",
            "  inflating: Dataset_LaPalma.csv     \n",
            "  inflating: Dataset_TFN.csv         \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oWgfVs_VwTcV"
      },
      "source": [
        "!mkdir data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDtcr6z4wMjt"
      },
      "source": [
        "!mv *.csv data/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmPwJmjHkve3"
      },
      "source": [
        "####Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "PKXqww3JkuQg",
        "outputId": "81f887d4-2385-41cc-8063-00d3b9a75a7d"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df_fuerte = pd.read_csv('data/Dataset_FUERTEVENTURA.csv')\n",
        "df_fuerte.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>17.0</td>\n",
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              "      <td>7.7</td>\n",
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              "             id                 time  temp     wd  ...  aa1_1  aa1_2  aa1_3  aa1_4\n",
              "0  600350-99999  1974-01-01 06:00:00  16.0  340.0  ...    NaN    NaN    NaN    NaN\n",
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              "\n",
              "[5 rows x 14 columns]"
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        "outputId": "d3ca6652-bbe9-42ac-ad6d-ce285b35fd17"
      },
      "source": [
        "df_fuerte.info()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 451628 entries, 0 to 451627\n",
            "Data columns (total 14 columns):\n",
            " #   Column      Non-Null Count   Dtype  \n",
            "---  ------      --------------   -----  \n",
            " 0   id          451628 non-null  object \n",
            " 1   time        451628 non-null  object \n",
            " 2   temp        450378 non-null  float64\n",
            " 3   wd          436017 non-null  float64\n",
            " 4   ws          450554 non-null  float64\n",
            " 5   atmos_pres  141715 non-null  float64\n",
            " 6   dew_point   449757 non-null  float64\n",
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            " 9   visibility  438948 non-null  float64\n",
            " 10  aa1_1       55709 non-null   float64\n",
            " 11  aa1_2       55709 non-null   float64\n",
            " 12  aa1_3       55709 non-null   float64\n",
            " 13  aa1_4       55709 non-null   float64\n",
            "dtypes: float64(12), object(2)\n",
            "memory usage: 48.2+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
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        "outputId": "cd5c92f8-0ba2-43b7-cb9f-1ba0c60054a6"
      },
      "source": [
        "df_gc = pd.read_csv('data/Dataset_GC.csv', sep=';')\n",
        "df_gc.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (3,7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
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            "text/plain": [
              "   Unnamed: 0            id                 time  ... aa1_2  aa1_3 aa1_4\n",
              "0           1  600300-99999  1974-01-01 00:00:00  ...     0    9.0   1.0\n",
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              "2           3  600300-99999  1974-01-01 09:00:00  ...   NaN    NaN   NaN\n",
              "3           4  600300-99999  1974-01-01 10:00:00  ...   NaN    NaN   NaN\n",
              "4           5  600300-99999  1974-01-01 11:00:00  ...   NaN    NaN   NaN\n",
              "\n",
              "[5 rows x 15 columns]"
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          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
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    },
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      "cell_type": "code",
      "metadata": {
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        },
        "id": "h_1qW4UVk96j",
        "outputId": "693e609d-4d53-4882-c266-1c9a36fe5554"
      },
      "source": [
        "df_gc.info()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 677963 entries, 0 to 677962\n",
            "Data columns (total 15 columns):\n",
            " #   Column      Non-Null Count   Dtype  \n",
            "---  ------      --------------   -----  \n",
            " 0   Unnamed: 0  677963 non-null  int64  \n",
            " 1   id          677963 non-null  object \n",
            " 2   time        677963 non-null  object \n",
            " 3   temp        675577 non-null  object \n",
            " 4   wd          630414 non-null  float64\n",
            " 5   ws          676732 non-null  object \n",
            " 6   atmos_pres  271336 non-null  object \n",
            " 7   dew_point   674527 non-null  object \n",
            " 8   rh          674472 non-null  object \n",
            " 9   ceil_hgt    487942 non-null  float64\n",
            " 10  visibility  625849 non-null  float64\n",
            " 11  aa1_1       85060 non-null   float64\n",
            " 12  aa1_2       85060 non-null   object \n",
            " 13  aa1_3       85060 non-null   float64\n",
            " 14  aa1_4       85060 non-null   float64\n",
            "dtypes: float64(6), int64(1), object(8)\n",
            "memory usage: 77.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 411
        },
        "id": "MwUF2O2Yk_4P",
        "outputId": "5adc6ac1-2446-442e-9bfa-f1a655b4a99b"
      },
      "source": [
        "df_gomera = pd.read_csv('data/Dataset_Gomera.csv', sep=';')\n",
        "df_gomera.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "dtypes: float64(6), int64(1), object(8)\n",
            "memory usage: 4.4+ MB\n"
          ],
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    {
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      "metadata": {
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        "outputId": "71c79099-a77f-4465-aa4a-776a43de2e6d"
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      "source": [
        "df_hierro = pd.read_csv('data/Dataset_Hierro.csv', sep=';')\n",
        "df_hierro.head()"
      ],
      "execution_count": 15,
      "outputs": [
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        "df_lanza = pd.read_csv('data/Dataset_LANZAROTE.csv')\n",
        "df_lanza.head()"
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      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (36,94) have mixed types.Specify dtype option on import or set low_memory=False.\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>600400-99999</td>\n",
              "      <td>1978-01-01 08:00:00</td>\n",
              "      <td>14.0</td>\n",
              "      <td>250.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>13.0</td>\n",
              "      <td>93.7</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>11200.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>600400-99999</td>\n",
              "      <td>1978-01-01 10:00:00</td>\n",
              "      <td>16.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>14.0</td>\n",
              "      <td>87.9</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>11200.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>600400-99999</td>\n",
              "      <td>1978-01-01 11:00:00</td>\n",
              "      <td>18.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>9.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.0</td>\n",
              "      <td>82.6</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>11200.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 114 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id                 time  temp     wd  ...  wg1_3  wg1_4  wg1_5  wg1_6\n",
              "0  600400-99999  1978-01-01 07:00:00  14.0  250.0  ...    NaN    NaN    NaN    NaN\n",
              "1  600400-99999  1978-01-01 08:00:00  14.0  250.0  ...    NaN    NaN    NaN    NaN\n",
              "2  600400-99999  1978-01-01 09:00:00  15.0  260.0  ...    NaN    NaN    NaN    NaN\n",
              "3  600400-99999  1978-01-01 10:00:00  16.0  200.0  ...    NaN    NaN    NaN    NaN\n",
              "4  600400-99999  1978-01-01 11:00:00  18.0  210.0  ...    NaN    NaN    NaN    NaN\n",
              "\n",
              "[5 rows x 114 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "16OCvwEPlMEh",
        "outputId": "22931d9c-4b59-4aa1-a1d6-71b7210517a5"
      },
      "source": [
        "df_lanza.info()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 489455 entries, 0 to 489454\n",
            "Columns: 114 entries, id to wg1_6\n",
            "dtypes: float64(109), object(5)\n",
            "memory usage: 425.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "uRL4-m_5lN1v",
        "outputId": "58f614c2-b811-4ca5-b593-a781e32176b4"
      },
      "source": [
        "df_lapalma = pd.read_csv('data/Dataset_LaPalma.csv', sep=';')\n",
        "df_lapalma.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ],
            "text/plain": [
              "   Unnamed: 0            id                 time  ... aa1_2  aa1_3 aa1_4\n",
              "0           1  600050-99999  1974-01-01 06:00:00  ...   NaN    NaN   NaN\n",
              "1           2  600050-99999  1974-01-01 08:00:00  ...   NaN    NaN   NaN\n",
              "2           3  600050-99999  1974-01-01 09:00:00  ...   NaN    NaN   NaN\n",
              "3           4  600050-99999  1974-01-01 10:00:00  ...   NaN    NaN   NaN\n",
              "4           5  600050-99999  1974-01-01 11:00:00  ...   NaN    NaN   NaN\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGGF5le-lRBH",
        "outputId": "95252c0e-08b8-41ea-d121-c0ba3747343d"
      },
      "source": [
        "df_lapalma.info()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 373063 entries, 0 to 373062\n",
            "Data columns (total 15 columns):\n",
            " #   Column      Non-Null Count   Dtype  \n",
            "---  ------      --------------   -----  \n",
            " 0   Unnamed: 0  373063 non-null  int64  \n",
            " 1   id          373063 non-null  object \n",
            " 2   time        373063 non-null  object \n",
            " 3   temp        372184 non-null  object \n",
            " 4   wd          345916 non-null  float64\n",
            " 5   ws          371693 non-null  object \n",
            " 6   atmos_pres  127020 non-null  object \n",
            " 7   dew_point   362183 non-null  object \n",
            " 8   rh          362153 non-null  object \n",
            " 9   ceil_hgt    262626 non-null  float64\n",
            " 10  visibility  333620 non-null  float64\n",
            " 11  aa1_1       57305 non-null   float64\n",
            " 12  aa1_2       57305 non-null   object \n",
            " 13  aa1_3       57305 non-null   float64\n",
            " 14  aa1_4       57305 non-null   float64\n",
            "dtypes: float64(6), int64(1), object(8)\n",
            "memory usage: 42.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "O_GEWK3AlSs0",
        "outputId": "fa5fcb2a-c4cf-447d-97d2-b5af1e290231"
      },
      "source": [
        "df_tfn = pd.read_csv('data/Dataset_TFN.csv', sep=';')\n",
        "df_tfn.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>temp_max</th>\n",
              "      <th>temp_avg</th>\n",
              "      <th>temp_min</th>\n",
              "      <th>prec</th>\n",
              "      <th>wd</th>\n",
              "      <th>ws</th>\n",
              "      <th>ws_max</th>\n",
              "      <th>atmos_pres_avg</th>\n",
              "      <th>atmos_pres_min</th>\n",
              "      <th>atmos_pres_max</th>\n",
              "      <th>rh</th>\n",
              "      <th>ceil_hgt</th>\n",
              "      <th>visibility</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1974-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>11,3333333333333</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>128</td>\n",
              "      <td>2,3</td>\n",
              "      <td>2,3</td>\n",
              "      <td>1022,32857142857</td>\n",
              "      <td>1020,8</td>\n",
              "      <td>1023,8</td>\n",
              "      <td>82,2166666666667</td>\n",
              "      <td>4116,66666666667</td>\n",
              "      <td>15383,3333333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1974-01-02</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>11,2</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>312,5</td>\n",
              "      <td>3,66666666666667</td>\n",
              "      <td>3,66666666666667</td>\n",
              "      <td>1020,375</td>\n",
              "      <td>1018,8</td>\n",
              "      <td>1021,9</td>\n",
              "      <td>84,5466666666667</td>\n",
              "      <td>16217,3333333333</td>\n",
              "      <td>12080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1974-01-03</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>15</td>\n",
              "      <td>11,952380952381</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>297,368421052632</td>\n",
              "      <td>5,84285714285714</td>\n",
              "      <td>5,84285714285714</td>\n",
              "      <td>1020,08571428571</td>\n",
              "      <td>1019,1</td>\n",
              "      <td>1021,9</td>\n",
              "      <td>83,6857142857143</td>\n",
              "      <td>18158,2352941176</td>\n",
              "      <td>10523,8095238095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1974-01-04</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>11,5714285714286</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>270</td>\n",
              "      <td>1,25714285714286</td>\n",
              "      <td>1,25714285714286</td>\n",
              "      <td>1022,65</td>\n",
              "      <td>1021,6</td>\n",
              "      <td>1024,1</td>\n",
              "      <td>82,3</td>\n",
              "      <td>22000</td>\n",
              "      <td>10400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1974-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>13,3684210526316</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>196,25</td>\n",
              "      <td>1,22105263157895</td>\n",
              "      <td>1,22105263157895</td>\n",
              "      <td>1025,1375</td>\n",
              "      <td>1024</td>\n",
              "      <td>1027,3</td>\n",
              "      <td>77,2894736842105</td>\n",
              "      <td>22000</td>\n",
              "      <td>10915,7894736842</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         year  month  day  ...                rh          ceil_hgt        visibility\n",
              "0  1974-01-01      1    1  ...  82,2166666666667  4116,66666666667  15383,3333333333\n",
              "1  1974-01-02      1    2  ...  84,5466666666667  16217,3333333333             12080\n",
              "2  1974-01-03      1    3  ...  83,6857142857143  18158,2352941176  10523,8095238095\n",
              "3  1974-01-04      1    4  ...              82,3             22000             10400\n",
              "4  1974-01-05      1    5  ...  77,2894736842105             22000  10915,7894736842\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QXyT-CEHlVZx",
        "outputId": "02e2d33c-f462-4e20-f4bf-afb334612c97"
      },
      "source": [
        "df_tfn.info()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16769 entries, 0 to 16768\n",
            "Data columns (total 16 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   year            16769 non-null  object\n",
            " 1   month           16769 non-null  int64 \n",
            " 2   day             16769 non-null  int64 \n",
            " 3   temp_max        16769 non-null  object\n",
            " 4   temp_avg        16769 non-null  object\n",
            " 5   temp_min        16769 non-null  object\n",
            " 6   prec            16769 non-null  object\n",
            " 7   wd              16769 non-null  object\n",
            " 8   ws              16769 non-null  object\n",
            " 9   ws_max          16769 non-null  object\n",
            " 10  atmos_pres_avg  16769 non-null  object\n",
            " 11  atmos_pres_min  16769 non-null  object\n",
            " 12  atmos_pres_max  16769 non-null  object\n",
            " 13  rh              16769 non-null  object\n",
            " 14  ceil_hgt        16769 non-null  object\n",
            " 15  visibility      16769 non-null  object\n",
            "dtypes: int64(2), object(14)\n",
            "memory usage: 2.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}