intro_time_series_valores_perdidos.ipynb

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        "<a href=\"https://colab.research.google.com/github/anadiedrichs/time-series-analysis/blob/master/intro_time_series_valores_perdidos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Actividad valores perdidos - Unidad análisis de series temporales - Teleinformática IoT - 2020\n",
        "\n",
        "**Docente: Ing. Ana Laura Diedrichs**\n",
        "\n",
        "\n",
        "Contacto por otros horarios de consulta o dudas: \n",
        "* Email: ana.diedrichs@frm.utn.edu.ar\n",
        "* Telegram  @anadiedrichs\n",
        "\n",
        "## Objetivos del laboratorio:\n",
        "\n",
        "* Analizar una serie temporal para un problema específico\n",
        "* Fomentar el trabajo en clase, discusión y aportes de soluciones entre los participantes\n",
        "* Ser una entrada en calor o introducción del uso del entorno colab.research.google.com y librerías Python como pandas\n",
        "\n",
        "## Pre-requisitos o pre-condiciones\n",
        "* Tener una cuenta google (gmail)\n",
        "* Tener instalado el navegador google chrome \n",
        "* Contar con conectividad a Internet\n",
        "\n",
        "## Entrega y uso del laboratorio\n",
        "\n",
        "**USO**\n",
        "\n",
        "* Antes que cualquier cosa, **cree una copia de este notebook: Click en *File*, luego *Save a Copy in Drive***\n",
        "* Renombre el archivo con el siguiente formato: APELLIDO_NOMBRE_LEGAJO_titulonotebook.ipynb \n",
        "Ejemplo: DIEDRICHS_ANA_99999_proyecto_forma_de_onda_2019.ipynb\n",
        "* Use el notebook, complete las actividades y consignas que se elija. \n",
        "* Este laboratorio es una actividad individual.\n",
        "* Se fomenta el intercambio de opiniones en clase y exposición.\n",
        "\n",
        "**ENTREGA**\n",
        "\n",
        "* Una vez finalizado el laboratorio, complete [el formulario de entrega](https://forms.gle/41zGpepnFmKpvfzo9) indicando\n",
        " * Apellido\n",
        " * Nombre\n",
        " * Nro Legajo\n",
        " * Carrera\n",
        " * Actividad (cuál laboratorio o actividad)\n",
        " * link de su notebook. El mismo se obtiene si realiza click en *Share* (esquina superior derecha) y luego en *Get shareable link* \n",
        " \n",
        " \n",
        " No se aceptarán otras formas de entrega distintas a la mencionada.\n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yRgKqWmw_88i",
        "colab_type": "text"
      },
      "source": [
        "# Missing values (valores perdidos)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "azNBCHgZFmca",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kCdpFoN3I5vG",
        "colab_type": "text"
      },
      "source": [
        "## Valores perdidos, missing values\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vuuZFr11LLat",
        "colab_type": "text"
      },
      "source": [
        "¿qué realizan los siguientes bloques de código?"
      ]
    },
    {
      "cell_type": "code",
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        "id": "4u_zkTgMKbCD",
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        "outputId": "b45d576a-7cd5-4fe9-97d1-d97c0e5eb549"
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      "source": [
        "df = pd.DataFrame({'date': pd.date_range(start='2013-01-01', periods=10, freq='H'), 'value': range(10)})\n",
        "df.loc[2:3, 'value'] = np.nan\n",
        "df.loc[6, 'value'] = np.nan\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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              "      <td>2013-01-01 06:00:00</td>\n",
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              "      <th>7</th>\n",
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              "      <td>7.0</td>\n",
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              "      <th>9</th>\n",
              "      <td>2013-01-01 09:00:00</td>\n",
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              "                 date  value\n",
              "0 2013-01-01 00:00:00    0.0\n",
              "1 2013-01-01 01:00:00    1.0\n",
              "2 2013-01-01 02:00:00    NaN\n",
              "3 2013-01-01 03:00:00    NaN\n",
              "4 2013-01-01 04:00:00    4.0\n",
              "5 2013-01-01 05:00:00    5.0\n",
              "6 2013-01-01 06:00:00    NaN\n",
              "7 2013-01-01 07:00:00    7.0\n",
              "8 2013-01-01 08:00:00    8.0\n",
              "9 2013-01-01 09:00:00    9.0"
            ]
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        "outputId": "41a5a191-eda0-4afe-cbd5-3f86e39495c3"
      },
      "source": [
        "df['value'].interpolate(method='linear', inplace=True)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>5.0</td>\n",
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              "      <th>6</th>\n",
              "      <td>2013-01-01 06:00:00</td>\n",
              "      <td>6.0</td>\n",
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              "      <th>7</th>\n",
              "      <td>2013-01-01 07:00:00</td>\n",
              "      <td>7.0</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2013-01-01 08:00:00</td>\n",
              "      <td>8.0</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2013-01-01 09:00:00</td>\n",
              "      <td>9.0</td>\n",
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            "text/plain": [
              "                 date  value\n",
              "0 2013-01-01 00:00:00    0.0\n",
              "1 2013-01-01 01:00:00    1.0\n",
              "2 2013-01-01 02:00:00    2.0\n",
              "3 2013-01-01 03:00:00    3.0\n",
              "4 2013-01-01 04:00:00    4.0\n",
              "5 2013-01-01 05:00:00    5.0\n",
              "6 2013-01-01 06:00:00    6.0\n",
              "7 2013-01-01 07:00:00    7.0\n",
              "8 2013-01-01 08:00:00    8.0\n",
              "9 2013-01-01 09:00:00    9.0"
            ]
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          "execution_count": 4
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qr2QSC64LPOD",
        "colab_type": "text"
      },
      "source": [
        "Ejecute los siguientes bloques de códigos y comente, ¿cuál es la diferencia entre el método bkfill y ffill y de ambos con interpolate?, "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XuLUBlXTM2Gx",
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          "height": 204
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        "outputId": "60f9ee46-9bbe-48ac-ad4c-677f18885f4e"
      },
      "source": [
        "time_index = pd.date_range('01/01/2010', periods=5, freq='M')\n",
        "\n",
        "# Create data frame, set index\n",
        "df = pd.DataFrame(index=time_index)\n",
        "\n",
        "# Create feature with a gap of missing values\n",
        "df['Sales'] = [1.0,2.0,np.nan,np.nan,5.0]\n",
        "df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Sales</th>\n",
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              "      <th>2010-01-31</th>\n",
              "      <td>1.0</td>\n",
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              "      <th>2010-02-28</th>\n",
              "      <td>2.0</td>\n",
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              "      <th>2010-03-31</th>\n",
              "      <td>NaN</td>\n",
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              "      <th>2010-04-30</th>\n",
              "      <td>NaN</td>\n",
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              "      <th>2010-05-31</th>\n",
              "      <td>5.0</td>\n",
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            "text/plain": [
              "            Sales\n",
              "2010-01-31    1.0\n",
              "2010-02-28    2.0\n",
              "2010-03-31    NaN\n",
              "2010-04-30    NaN\n",
              "2010-05-31    5.0"
            ]
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          "metadata": {
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          "execution_count": 5
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          "height": 204
        },
        "outputId": "bd5bf544-9cae-4158-9cdb-64c9cbbd105f"
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      "source": [
        "df.interpolate(method=\"linear\")"
      ],
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          "output_type": "execute_result",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-31</th>\n",
              "      <td>1.0</td>\n",
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              "      <th>2010-02-28</th>\n",
              "      <td>2.0</td>\n",
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              "      <td>3.0</td>\n",
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              "      <th>2010-04-30</th>\n",
              "      <td>4.0</td>\n",
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              "      <th>2010-05-31</th>\n",
              "      <td>5.0</td>\n",
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            "text/plain": [
              "            Sales\n",
              "2010-01-31    1.0\n",
              "2010-02-28    2.0\n",
              "2010-03-31    3.0\n",
              "2010-04-30    4.0\n",
              "2010-05-31    5.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XdVbexzSOINa",
        "colab": {}
      },
      "source": [
        "df.ffill()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RrQS7KlKNVss",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.bfill()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_W9rW-W7OYQR",
        "colab_type": "text"
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      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ci43nT0qLoSB",
        "colab_type": "code",
        "colab": {
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          "height": 1000
        },
        "outputId": "1bfb63c3-962b-4239-fc92-83b99af095c2"
      },
      "source": [
        "N = 50 \n",
        "time_index = pd.date_range('01/01/2010', periods=N, freq='H')\n",
        "# Create data frame, set index\n",
        "df2 = pd.DataFrame(index=time_index)\n",
        "x = np.linspace(0, 2*np.pi, N)\n",
        "y = np.sin(x)\n",
        "# Create feature with a gap of missing valuesH\n",
        "df2['value'] = y  # [1.0,2.0,np.nan,np.nan,5.0]\n",
        "df2.loc[2:3, 'value'] = np.nan\n",
        "df2.loc[6:10, 'value'] = np.nan\n",
        "df2.loc[22:33, 'value'] = np.nan\n",
        "df2.loc[46:50, 'value'] = np.nan\n",
        "df2\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>2010-01-01 00:00:00</th>\n",
              "      <td>0.000000</td>\n",
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              "      <th>2010-01-01 01:00:00</th>\n",
              "      <td>0.127877</td>\n",
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              "      <th>2010-01-01 02:00:00</th>\n",
              "      <td>NaN</td>\n",
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              "      <th>2010-01-01 03:00:00</th>\n",
              "      <td>0.375267</td>\n",
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              "    <tr>\n",
              "      <th>2010-01-01 04:00:00</th>\n",
              "      <td>0.490718</td>\n",
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              "    <tr>\n",
              "      <th>2010-01-01 05:00:00</th>\n",
              "      <td>0.598111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 06:00:00</th>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2010-01-01 07:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 08:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 09:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 10:00:00</th>\n",
              "      <td>0.958668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 11:00:00</th>\n",
              "      <td>0.987182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 12:00:00</th>\n",
              "      <td>0.999486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 13:00:00</th>\n",
              "      <td>0.995379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 14:00:00</th>\n",
              "      <td>0.974928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 15:00:00</th>\n",
              "      <td>0.938468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 16:00:00</th>\n",
              "      <td>0.886599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 17:00:00</th>\n",
              "      <td>0.820172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 18:00:00</th>\n",
              "      <td>0.740278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 19:00:00</th>\n",
              "      <td>0.648228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 20:00:00</th>\n",
              "      <td>0.545535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 21:00:00</th>\n",
              "      <td>0.433884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 22:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 23:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 00:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 01:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 02:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 03:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 04:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 05:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 06:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 07:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 08:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 09:00:00</th>\n",
              "      <td>-0.886599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 10:00:00</th>\n",
              "      <td>-0.938468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 11:00:00</th>\n",
              "      <td>-0.974928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 12:00:00</th>\n",
              "      <td>-0.995379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 13:00:00</th>\n",
              "      <td>-0.999486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 14:00:00</th>\n",
              "      <td>-0.987182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 15:00:00</th>\n",
              "      <td>-0.958668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 16:00:00</th>\n",
              "      <td>-0.914413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 17:00:00</th>\n",
              "      <td>-0.855143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 18:00:00</th>\n",
              "      <td>-0.781831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 19:00:00</th>\n",
              "      <td>-0.695683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 20:00:00</th>\n",
              "      <td>-0.598111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 21:00:00</th>\n",
              "      <td>-0.490718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 22:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 23:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-03 00:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-03 01:00:00</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        value\n",
              "2010-01-01 00:00:00  0.000000\n",
              "2010-01-01 01:00:00  0.127877\n",
              "2010-01-01 02:00:00       NaN\n",
              "2010-01-01 03:00:00  0.375267\n",
              "2010-01-01 04:00:00  0.490718\n",
              "2010-01-01 05:00:00  0.598111\n",
              "2010-01-01 06:00:00       NaN\n",
              "2010-01-01 07:00:00       NaN\n",
              "2010-01-01 08:00:00       NaN\n",
              "2010-01-01 09:00:00       NaN\n",
              "2010-01-01 10:00:00  0.958668\n",
              "2010-01-01 11:00:00  0.987182\n",
              "2010-01-01 12:00:00  0.999486\n",
              "2010-01-01 13:00:00  0.995379\n",
              "2010-01-01 14:00:00  0.974928\n",
              "2010-01-01 15:00:00  0.938468\n",
              "2010-01-01 16:00:00  0.886599\n",
              "2010-01-01 17:00:00  0.820172\n",
              "2010-01-01 18:00:00  0.740278\n",
              "2010-01-01 19:00:00  0.648228\n",
              "2010-01-01 20:00:00  0.545535\n",
              "2010-01-01 21:00:00  0.433884\n",
              "2010-01-01 22:00:00       NaN\n",
              "2010-01-01 23:00:00       NaN\n",
              "2010-01-02 00:00:00       NaN\n",
              "2010-01-02 01:00:00       NaN\n",
              "2010-01-02 02:00:00       NaN\n",
              "2010-01-02 03:00:00       NaN\n",
              "2010-01-02 04:00:00       NaN\n",
              "2010-01-02 05:00:00       NaN\n",
              "2010-01-02 06:00:00       NaN\n",
              "2010-01-02 07:00:00       NaN\n",
              "2010-01-02 08:00:00       NaN\n",
              "2010-01-02 09:00:00 -0.886599\n",
              "2010-01-02 10:00:00 -0.938468\n",
              "2010-01-02 11:00:00 -0.974928\n",
              "2010-01-02 12:00:00 -0.995379\n",
              "2010-01-02 13:00:00 -0.999486\n",
              "2010-01-02 14:00:00 -0.987182\n",
              "2010-01-02 15:00:00 -0.958668\n",
              "2010-01-02 16:00:00 -0.914413\n",
              "2010-01-02 17:00:00 -0.855143\n",
              "2010-01-02 18:00:00 -0.781831\n",
              "2010-01-02 19:00:00 -0.695683\n",
              "2010-01-02 20:00:00 -0.598111\n",
              "2010-01-02 21:00:00 -0.490718\n",
              "2010-01-02 22:00:00       NaN\n",
              "2010-01-02 23:00:00       NaN\n",
              "2010-01-03 00:00:00       NaN\n",
              "2010-01-03 01:00:00       NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvQOdY5LOb4K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5f402f5e-635f-4e23-f179-df09e049b09f"
      },
      "source": [
        "time_index = pd.date_range('01/01/2010', periods=N, freq='H')\n",
        "dfcomparacion = pd.DataFrame(index=time_index)\n",
        "# Create feature with a gap of missing valuesH\n",
        "dfcomparacion['real'] = y  # [1.0,2.0,np.nan,np.nan,5.0]\n",
        "dfcomparacion['missing'] = y  # [1.0,2.0,np.nan,np.nan,5.0]\n",
        "dfcomparacion.loc[2:3, 'missing'] = np.nan\n",
        "dfcomparacion.loc[6:10, 'missing'] = np.nan\n",
        "dfcomparacion.loc[22:33, 'missing'] = np.nan\n",
        "dfcomparacion.loc[46:50, 'missing'] = np.nan\n",
        "\n",
        "dfcomparacion.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>real</th>\n",
              "      <th>missing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-01 00:00:00</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 01:00:00</th>\n",
              "      <td>0.127877</td>\n",
              "      <td>0.127877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 02:00:00</th>\n",
              "      <td>0.253655</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 03:00:00</th>\n",
              "      <td>0.375267</td>\n",
              "      <td>0.375267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 04:00:00</th>\n",
              "      <td>0.490718</td>\n",
              "      <td>0.490718</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         real   missing\n",
              "2010-01-01 00:00:00  0.000000  0.000000\n",
              "2010-01-01 01:00:00  0.127877  0.127877\n",
              "2010-01-01 02:00:00  0.253655       NaN\n",
              "2010-01-01 03:00:00  0.375267  0.375267\n",
              "2010-01-01 04:00:00  0.490718  0.490718"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xD6Zwp3MwLRE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b5b195cd-0026-4f18-a6e8-4042d9f13320"
      },
      "source": [
        "dfcomparacion['interpolacion'] = dfcomparacion['missing'].interpolate(method=\"linear\")\n",
        "dfcomparacion['int2'] = dfcomparacion['missing'].interpolate(method=\"time\")\n",
        "\n",
        "dfcomparacion"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>real</th>\n",
              "      <th>missing</th>\n",
              "      <th>interpolacion</th>\n",
              "      <th>int2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-01 00:00:00</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 01:00:00</th>\n",
              "      <td>1.278772e-01</td>\n",
              "      <td>0.127877</td>\n",
              "      <td>0.127877</td>\n",
              "      <td>0.127877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 02:00:00</th>\n",
              "      <td>2.536546e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.251572</td>\n",
              "      <td>0.251572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 03:00:00</th>\n",
              "      <td>3.752670e-01</td>\n",
              "      <td>0.375267</td>\n",
              "      <td>0.375267</td>\n",
              "      <td>0.375267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 04:00:00</th>\n",
              "      <td>4.907176e-01</td>\n",
              "      <td>0.490718</td>\n",
              "      <td>0.490718</td>\n",
              "      <td>0.490718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 05:00:00</th>\n",
              "      <td>5.981105e-01</td>\n",
              "      <td>0.598111</td>\n",
              "      <td>0.598111</td>\n",
              "      <td>0.598111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 06:00:00</th>\n",
              "      <td>6.956826e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.670222</td>\n",
              "      <td>0.670222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 07:00:00</th>\n",
              "      <td>7.818315e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.742333</td>\n",
              "      <td>0.742333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 08:00:00</th>\n",
              "      <td>8.551428e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.814445</td>\n",
              "      <td>0.814445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 09:00:00</th>\n",
              "      <td>9.144126e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.886556</td>\n",
              "      <td>0.886556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 10:00:00</th>\n",
              "      <td>9.586679e-01</td>\n",
              "      <td>0.958668</td>\n",
              "      <td>0.958668</td>\n",
              "      <td>0.958668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 11:00:00</th>\n",
              "      <td>9.871818e-01</td>\n",
              "      <td>0.987182</td>\n",
              "      <td>0.987182</td>\n",
              "      <td>0.987182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 12:00:00</th>\n",
              "      <td>9.994862e-01</td>\n",
              "      <td>0.999486</td>\n",
              "      <td>0.999486</td>\n",
              "      <td>0.999486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 13:00:00</th>\n",
              "      <td>9.953791e-01</td>\n",
              "      <td>0.995379</td>\n",
              "      <td>0.995379</td>\n",
              "      <td>0.995379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 14:00:00</th>\n",
              "      <td>9.749279e-01</td>\n",
              "      <td>0.974928</td>\n",
              "      <td>0.974928</td>\n",
              "      <td>0.974928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 15:00:00</th>\n",
              "      <td>9.384684e-01</td>\n",
              "      <td>0.938468</td>\n",
              "      <td>0.938468</td>\n",
              "      <td>0.938468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 16:00:00</th>\n",
              "      <td>8.865993e-01</td>\n",
              "      <td>0.886599</td>\n",
              "      <td>0.886599</td>\n",
              "      <td>0.886599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 17:00:00</th>\n",
              "      <td>8.201723e-01</td>\n",
              "      <td>0.820172</td>\n",
              "      <td>0.820172</td>\n",
              "      <td>0.820172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 18:00:00</th>\n",
              "      <td>7.402780e-01</td>\n",
              "      <td>0.740278</td>\n",
              "      <td>0.740278</td>\n",
              "      <td>0.740278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 19:00:00</th>\n",
              "      <td>6.482284e-01</td>\n",
              "      <td>0.648228</td>\n",
              "      <td>0.648228</td>\n",
              "      <td>0.648228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 20:00:00</th>\n",
              "      <td>5.455349e-01</td>\n",
              "      <td>0.545535</td>\n",
              "      <td>0.545535</td>\n",
              "      <td>0.545535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 21:00:00</th>\n",
              "      <td>4.338837e-01</td>\n",
              "      <td>0.433884</td>\n",
              "      <td>0.433884</td>\n",
              "      <td>0.433884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 22:00:00</th>\n",
              "      <td>3.151082e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.323843</td>\n",
              "      <td>0.323843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-01 23:00:00</th>\n",
              "      <td>1.911586e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.213803</td>\n",
              "      <td>0.213803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 00:00:00</th>\n",
              "      <td>6.407022e-02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.103763</td>\n",
              "      <td>0.103763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 01:00:00</th>\n",
              "      <td>-6.407022e-02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.006277</td>\n",
              "      <td>-0.006277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 02:00:00</th>\n",
              "      <td>-1.911586e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.116318</td>\n",
              "      <td>-0.116318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 03:00:00</th>\n",
              "      <td>-3.151082e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.226358</td>\n",
              "      <td>-0.226358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 04:00:00</th>\n",
              "      <td>-4.338837e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.336398</td>\n",
              "      <td>-0.336398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 05:00:00</th>\n",
              "      <td>-5.455349e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.446438</td>\n",
              "      <td>-0.446438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 06:00:00</th>\n",
              "      <td>-6.482284e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.556479</td>\n",
              "      <td>-0.556479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 07:00:00</th>\n",
              "      <td>-7.402780e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.666519</td>\n",
              "      <td>-0.666519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 08:00:00</th>\n",
              "      <td>-8.201723e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.776559</td>\n",
              "      <td>-0.776559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 09:00:00</th>\n",
              "      <td>-8.865993e-01</td>\n",
              "      <td>-0.886599</td>\n",
              "      <td>-0.886599</td>\n",
              "      <td>-0.886599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 10:00:00</th>\n",
              "      <td>-9.384684e-01</td>\n",
              "      <td>-0.938468</td>\n",
              "      <td>-0.938468</td>\n",
              "      <td>-0.938468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 11:00:00</th>\n",
              "      <td>-9.749279e-01</td>\n",
              "      <td>-0.974928</td>\n",
              "      <td>-0.974928</td>\n",
              "      <td>-0.974928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 12:00:00</th>\n",
              "      <td>-9.953791e-01</td>\n",
              "      <td>-0.995379</td>\n",
              "      <td>-0.995379</td>\n",
              "      <td>-0.995379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 13:00:00</th>\n",
              "      <td>-9.994862e-01</td>\n",
              "      <td>-0.999486</td>\n",
              "      <td>-0.999486</td>\n",
              "      <td>-0.999486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 14:00:00</th>\n",
              "      <td>-9.871818e-01</td>\n",
              "      <td>-0.987182</td>\n",
              "      <td>-0.987182</td>\n",
              "      <td>-0.987182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 15:00:00</th>\n",
              "      <td>-9.586679e-01</td>\n",
              "      <td>-0.958668</td>\n",
              "      <td>-0.958668</td>\n",
              "      <td>-0.958668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 16:00:00</th>\n",
              "      <td>-9.144126e-01</td>\n",
              "      <td>-0.914413</td>\n",
              "      <td>-0.914413</td>\n",
              "      <td>-0.914413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 17:00:00</th>\n",
              "      <td>-8.551428e-01</td>\n",
              "      <td>-0.855143</td>\n",
              "      <td>-0.855143</td>\n",
              "      <td>-0.855143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 18:00:00</th>\n",
              "      <td>-7.818315e-01</td>\n",
              "      <td>-0.781831</td>\n",
              "      <td>-0.781831</td>\n",
              "      <td>-0.781831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 19:00:00</th>\n",
              "      <td>-6.956826e-01</td>\n",
              "      <td>-0.695683</td>\n",
              "      <td>-0.695683</td>\n",
              "      <td>-0.695683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 20:00:00</th>\n",
              "      <td>-5.981105e-01</td>\n",
              "      <td>-0.598111</td>\n",
              "      <td>-0.598111</td>\n",
              "      <td>-0.598111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 21:00:00</th>\n",
              "      <td>-4.907176e-01</td>\n",
              "      <td>-0.490718</td>\n",
              "      <td>-0.490718</td>\n",
              "      <td>-0.490718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 22:00:00</th>\n",
              "      <td>-3.752670e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.490718</td>\n",
              "      <td>-0.490718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-02 23:00:00</th>\n",
              "      <td>-2.536546e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.490718</td>\n",
              "      <td>-0.490718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-03 00:00:00</th>\n",
              "      <td>-1.278772e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.490718</td>\n",
              "      <td>-0.490718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-03 01:00:00</th>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.490718</td>\n",
              "      <td>-0.490718</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             real   missing  interpolacion      int2\n",
              "2010-01-01 00:00:00  0.000000e+00  0.000000       0.000000  0.000000\n",
              "2010-01-01 01:00:00  1.278772e-01  0.127877       0.127877  0.127877\n",
              "2010-01-01 02:00:00  2.536546e-01       NaN       0.251572  0.251572\n",
              "2010-01-01 03:00:00  3.752670e-01  0.375267       0.375267  0.375267\n",
              "2010-01-01 04:00:00  4.907176e-01  0.490718       0.490718  0.490718\n",
              "2010-01-01 05:00:00  5.981105e-01  0.598111       0.598111  0.598111\n",
              "2010-01-01 06:00:00  6.956826e-01       NaN       0.670222  0.670222\n",
              "2010-01-01 07:00:00  7.818315e-01       NaN       0.742333  0.742333\n",
              "2010-01-01 08:00:00  8.551428e-01       NaN       0.814445  0.814445\n",
              "2010-01-01 09:00:00  9.144126e-01       NaN       0.886556  0.886556\n",
              "2010-01-01 10:00:00  9.586679e-01  0.958668       0.958668  0.958668\n",
              "2010-01-01 11:00:00  9.871818e-01  0.987182       0.987182  0.987182\n",
              "2010-01-01 12:00:00  9.994862e-01  0.999486       0.999486  0.999486\n",
              "2010-01-01 13:00:00  9.953791e-01  0.995379       0.995379  0.995379\n",
              "2010-01-01 14:00:00  9.749279e-01  0.974928       0.974928  0.974928\n",
              "2010-01-01 15:00:00  9.384684e-01  0.938468       0.938468  0.938468\n",
              "2010-01-01 16:00:00  8.865993e-01  0.886599       0.886599  0.886599\n",
              "2010-01-01 17:00:00  8.201723e-01  0.820172       0.820172  0.820172\n",
              "2010-01-01 18:00:00  7.402780e-01  0.740278       0.740278  0.740278\n",
              "2010-01-01 19:00:00  6.482284e-01  0.648228       0.648228  0.648228\n",
              "2010-01-01 20:00:00  5.455349e-01  0.545535       0.545535  0.545535\n",
              "2010-01-01 21:00:00  4.338837e-01  0.433884       0.433884  0.433884\n",
              "2010-01-01 22:00:00  3.151082e-01       NaN       0.323843  0.323843\n",
              "2010-01-01 23:00:00  1.911586e-01       NaN       0.213803  0.213803\n",
              "2010-01-02 00:00:00  6.407022e-02       NaN       0.103763  0.103763\n",
              "2010-01-02 01:00:00 -6.407022e-02       NaN      -0.006277 -0.006277\n",
              "2010-01-02 02:00:00 -1.911586e-01       NaN      -0.116318 -0.116318\n",
              "2010-01-02 03:00:00 -3.151082e-01       NaN      -0.226358 -0.226358\n",
              "2010-01-02 04:00:00 -4.338837e-01       NaN      -0.336398 -0.336398\n",
              "2010-01-02 05:00:00 -5.455349e-01       NaN      -0.446438 -0.446438\n",
              "2010-01-02 06:00:00 -6.482284e-01       NaN      -0.556479 -0.556479\n",
              "2010-01-02 07:00:00 -7.402780e-01       NaN      -0.666519 -0.666519\n",
              "2010-01-02 08:00:00 -8.201723e-01       NaN      -0.776559 -0.776559\n",
              "2010-01-02 09:00:00 -8.865993e-01 -0.886599      -0.886599 -0.886599\n",
              "2010-01-02 10:00:00 -9.384684e-01 -0.938468      -0.938468 -0.938468\n",
              "2010-01-02 11:00:00 -9.749279e-01 -0.974928      -0.974928 -0.974928\n",
              "2010-01-02 12:00:00 -9.953791e-01 -0.995379      -0.995379 -0.995379\n",
              "2010-01-02 13:00:00 -9.994862e-01 -0.999486      -0.999486 -0.999486\n",
              "2010-01-02 14:00:00 -9.871818e-01 -0.987182      -0.987182 -0.987182\n",
              "2010-01-02 15:00:00 -9.586679e-01 -0.958668      -0.958668 -0.958668\n",
              "2010-01-02 16:00:00 -9.144126e-01 -0.914413      -0.914413 -0.914413\n",
              "2010-01-02 17:00:00 -8.551428e-01 -0.855143      -0.855143 -0.855143\n",
              "2010-01-02 18:00:00 -7.818315e-01 -0.781831      -0.781831 -0.781831\n",
              "2010-01-02 19:00:00 -6.956826e-01 -0.695683      -0.695683 -0.695683\n",
              "2010-01-02 20:00:00 -5.981105e-01 -0.598111      -0.598111 -0.598111\n",
              "2010-01-02 21:00:00 -4.907176e-01 -0.490718      -0.490718 -0.490718\n",
              "2010-01-02 22:00:00 -3.752670e-01       NaN      -0.490718 -0.490718\n",
              "2010-01-02 23:00:00 -2.536546e-01       NaN      -0.490718 -0.490718\n",
              "2010-01-03 00:00:00 -1.278772e-01       NaN      -0.490718 -0.490718\n",
              "2010-01-03 01:00:00 -2.449294e-16       NaN      -0.490718 -0.490718"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fy6e1lhewRFT",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUxEOSVpTRx9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "outputId": "a018cdb5-6312-4d39-d76f-f039c5be5071"
      },
      "source": [
        "plt.plot(dfcomparacion)\n",
        "plt.grid()\n",
        "plt.show()\n",
        "dfcomparacion.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/plotting/_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
            "\n",
            "To register the converters:\n",
            "\t>>> from pandas.plotting import register_matplotlib_converters\n",
            "\t>>> register_matplotlib_converters()\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5bF4cnh6GR66rVWDbLJZnPivlSve2rkpwSR8+mhNH+wEvEV/ZH99xk3X4qrpi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\nlFJKXUALg1JKqQv8H42YyICYEp9wAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb2d2285eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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71ORZxERdNDqSELlGlouFUsoGmAK0BaoAPZVSVe5ZbC8QqLWuDiwCJqabF6e1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\nvDKEtCO6ENStPydrFafkwh2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MpULNJkbHy3Hz3m5LjT9OccTPnvZz/7L6E9pKqT1a68DH\n6St3cAsh7nC0s+G7FwIp5uLIK7ODKf5/n5Ngrzg5/H1SUpKNjpejFgzvRo0/TnGsvA2tf95q9YUi\nq6RYCCHuUqSgAz+8WAelFK/9fokzz7Wj9MkbbJ420uhoOWbx2Jeo8tshTpY28eTP63Au5Gp0JMNJ\nsRBC/IePZ0Hmv1YfOxsTo2+14JR3QVxnLiXyXJjR0bLd8i/fxvfXvzhXXFHnx2UULlLC6Ei5ghQL\nIUSGfDwLsvD1IFyc7Pmyak8cEjU7h75udKxstea7UZT+bg2R7lB5xlyKlvIxOlKuIcVCCHFfpd0L\nsPD1IBKL1eK3AB98dp1j1+8zjI6VLZaM70uxLxcQXRC8vp6e50eRfVRSLIQQD1TC1Yl5r9Xn75pv\ncd7NhvjxX3HrRpTRsSwmJTmZef1bUfHHP4l0VxT/diqVAp4wOlauI8VCCJGpooUcmfNmcxY2eQbP\naymsePc5oyNZRNytGBb3qkeNdWc5XsGW2gvWUCkfXiL8MKRYCCEeiruzPRNGfsD+Kk5U3XqKea80\nJsGKn6x38cxx1ndtQLV9sRwIdKH9b8G4FyttdKxcS4qFEOKhuTrZ0fybRYRWsqfGtius71SHY/v/\nNDrWIzv092oOP9cR71MpHGjvQ/dfdmJnf99BrwVSLIQQj8izZHk6LdrDgY6V8DqfStRLfVn57X0H\njM511v80jhv9BuEWDSf7tqT75yuMjmQVpFgIIR6Zja0t3ScuJXr0WyTYQ7mvlzHv1SdIjI8zOtp9\nXbkQzvxedSnxyc+kmuDW6H50fOcro2NZDSkWQojH9kTXflSavyztsNSfkaztGMjx/duNjvUfy798\nmyOd21E9+AYh1ZwoM3chjZ9+y+hYVkWKhRAiS4qX8U07LPVURUqfT+Van1eY91ZzrkWeMzoap44E\ns/jpACpMXUOqCcLf7kK3Bf/kuwcXWYKMOiuEsJg/l0wleuo3+JxJ5VpBONvEl44f/oKTs0uO5khJ\nTua3j16gzPK9FIiHw/XcaPPZ4nw/dEdWRp2VYiGEsLgVU97DbuFKylzUXHaDyNa16TRsVrZfcXTl\nQjgbPh9IkV3HKXUZzhZTOPZ/kye65u9nctxm2BDlSqluSqnDSqlUpdR9Ayil2iilQpVSYUqpIena\nvZVSO83t85VS9lnJI4TIHTq89Skt1h/gSO+GpJrAf94etrQIYOHInpw9YfkHYu5Y+QMLegZyuk07\nqq84jikVDnTw5ck/gqVQWEiW9iyUUpWBVGA68K7W+j+/7iulbIBjQEsgAtgN9NRahyilFgC/aa3n\nKaWmAfu11lMz267sWQhhPRLiYvn9496UWH8Ij2hIUXCmlImYSsUp064X9Vo/j42t7SOvN3TfVg4u\nnUGB7XvxPptKsgmOV7SnQPuOtHxx1GOtM68z/DCUUmoz9y8WQcBorXVr8/RQ86zxQCRQXGudfO9y\nDyLFQgjrkxAXy+ZfJ3Jt6xqKnrhOiStp7ZGF4WL5gugSxTAVdMHexQ3Hwp44e5TErXg5CrkX4/iu\nNVw98BdEROB8ORaPq6m4mG8ev+oCZ2sVI/CNsfjWaGjcB7QCWSkWOVF6SwFn001HAPWAIsB1rXVy\nuvZSOZBHCGEAB6cCtH55NLw8GoB9f/7O0d+m43zkNJX238T+n5sZ9rsFlDS/EuzgsrvibHlHkkt4\n4uIfSPPeH9Aonz/FLidkWiyUUuuB4hnMGq61/t3yke6boy/QF6BMmTI5tVkhRDYJaNyJgMadAIi5\ndplzYQe4fuk0MZfPEX8tkqQbUSTfjIG4WOxKlqZ0nRbUaPw0AVIYDJFpsdBat8jiNs4B6Ufn8jK3\nXQUKK6VszXsXt9vvl2MGMAPSDkNlMZMQIhdxcSuKS52s/lcjslNO3JS3G/A1X/lkD/QAlum0kyWb\ngK7m5XoDObanIoQQ4uFl9dLZLkqpCCAIWKmUWmNuL6mUWgVg3mvoB6wBjgALtNaHzasYDAxSSoWR\ndg5jVlbyCCGEyB5yU54QQuQTht2UJ4QQIn+QYiGEECJTUiyEEEJkSoqFEEKITEmxEEIIkSmrvBpK\nKRUHHM50QWO5AtFGh8iEZLSMMsAZo0Nkwhp+jpLRMh70fayktS70OCu11mIRqbX2NDrHgyilZmit\n+xqd40Eko2XI99EyJKNlPOj7qJQKzm+Xzl43OsBDWG50gIcgGS1Dvo+WIRktI1u+j9a6Z/HY1VEI\nS5Pvo8hNHvR9zI97FjOMDiBEOvJ9FLnJg76Pj/1dtco9CyGEEDnLWvcsclRGzxBXacYqpY4ppY4o\npQbcp29vpdRx86t3uvbaSqmD5nV+pZRSWcz4vVLqslLqULq2T5VSR5VSB5RSS5RShR/285nbLfqM\n9PtkDFBK/a2U2qeUClZK1b1P3xz5OVoD+T7K99EQWmtDX0AbIBQIA4aY27yBnea2+YD9ffoONS8T\nCrR+0DqzkM8GOAGUB+yB/UAV4EXgJ8BkXq5oBn3dgZPmP93M793M83YB9QEFrAbaZjHnE0At4FC6\ntlaArfn9BGDCw34+81kQvtUAAAeLSURBVLwFQA/z+2nAG9mQce3tzw60AzYb/HOU76N8H+X7mNH6\nsvJBsvp6wBc/0y+Febn9gIP5h3fCvL77ftkeM2MQsOaev4Ch5i9FhUz69gSmp5uebm4rARy933JZ\nyFou/Rf/nnldgDmP8PkUcCXdP+67lrNURtKGru+e7ufwq1E/R/k+yvdRvo/3fxl9GKouEKa1Pqm1\nTgT+v727D5GrOuM4/v2R2FSstqkvUVNrtQZCbJO2hqIgWO2LNchGEHxFo9EWLUGNiIKBigGhUCmF\n+lJotOSPULG+Bo2G/qEYQiKalzVq2hrbFF/SiihJrZhqfPzjnM1O4r1zZ3dn5t7s/j4wZHLm3rNn\n7j47Z+499zznAWA+cBbwUN5mOXBewb7zgQciYndE/JPUS36/TZ2jVbSG+HTgm8CF+VT1KUkzACTN\nlbSsYt/p+fn+5b20kPRNZ5/1Rtq0sV9rpN8A/FrSG8CdpA+Guo6j43Hf8l5yPFZrVDzW3VmUHfTC\noJA0IGlpxb5l5d02Bfgo0m1ofwDuB4iIFyPi6h78vFGTtAT4BFgBEBFvR8S8elu117XA4og4DlhM\nXgCrpuPoeOwDx2PHGhWPdXcWIxIRKyPil33+sWVriL8JPJLLHgVmj2Dft/Lz/cu7TtIVwLnApZHP\nTzts49410nvcxgUMH8c/k775dNrGvh3HIo7HkXM89k6v47HuzqLsoHcSFO1+YUXlo1W4hjjwGHBm\n3uYM4O8F+64GfiJpqqSppAG+1RGxA9gl6dR8t8Tl9GD9cUk/BW4GBiLiw5LN6l4j/W3S8YN0ev1a\nwTb9Oo6OR8ej47HMWAeIxvIAJpPuJDiB4cGWk0k9eusAzi8K9j2ZfQdw/kEavCmsc4ztnEf643sd\nWJLLvgI8CWwB1gFzcvlcYFnLvgtJ1wu3AVe2lM8FXs513kWe8zKGNv4J2AF8TPqWeVX+mW8Am/Pj\n93nbY4FV7d5fLj+RNHC6Lf9OpvSgjacDG/Lv6XnglLqOo+PR8eh4bNOesRzsbjxKAr8wKIABYGnL\nvkvyfn+j5Ra1smDzw4+qh+PRjyY9mhSPnsFtZmaV6h6zMDOzA4A7CzMzq+TOwszMKtXaWZQkRFuU\n/x+Sjmiz77OSvIaAdU1JPK7IZS/nxHMHlezreLSuKonH+yQN5mSMD0n6Usm+29t9fo5GbZ2FpEnA\n3cA5pDwmF0uaBawFfgT8q6622cTTJh5XADOBbwMHA42aDW3jU5t4XBwRcyJiNmmd7UX9alOdZxaF\nOUoiYlNEbB9JRZLuzTlxXpF0e0v5dkm3S9qY0wbP7O5bsHGkLB5XRUa6XfFrbWvB8WhdURaPuyCl\npCd9eam8nVXSY5I25Hj8eUv5B0pp7QeV0rJPa1dPnZ1FN3PmLImUE2c2cIak1lQH70bE94B7gZtG\nWb+Nf23jMV9+ugx4uoO6HI82VqXxKOmPwL9JZ7y/66CuhRFxCmnC4HWSDs/lhwDrI2IO8Bzws3aV\njJcB7gskbQQ2kWYuzmp5bSjPywZSOmKz0bgHeC4i1nSwrePReiYiriTNet8KXNjBLtdJGgTWk1J9\nzMjl/weeyM8r47HOzmJEOUokrVZavWrZfuUnkL6h/TBfx3sS+GLLJrvzv3tIU93NipTGo6TbgCOB\nG4dedDxaj7X9fIyIPaRLU+dLmpRjcbOGs84CIOkHpDHg0/IZxCaG4/HjGJ6VXRmPdQbr3oRhpINw\nEXBJ2cYRcXbJS4cB/wN25mtu5wDPdrepNgEUxqOkq4GzSR/+nw5t7Hi0HiuLx5MiYlsesxggLba0\nB/hOST1fBt6PiA/zGNmpo21QbZ1FRHwiaREpg+Mk4P6IeEVp7eCbgaOBlyStiuI88pOB3RGxRdIm\n4K+ka3xr+/QWbBxpE4+DpDvz1qW/Tx6JiKUFVTgerWuK4pF02WmNpMNIKwcOktbfKDKZdBb7NHCN\npK2kHFHrR9umAzI3lKQppCRa34qInXW3xyY2x6M1iaQjgc0R0dVFtg64Ae488WkzcI//MK1ujkdr\nEkkDwBrycrBdrftAPLMwM7P+asSZhaTjJD0j6dU8ceT6XP5VSX+R9Fr+d2ounylpnaTdkm7ar67P\nTZE3M7OxacSZhaRjgGMiYqOkQ0n3/J4HXAG8FxG/yh/8UyPiFklHAcfnbd6PiDtzPZNIi3r8mDSJ\n5QXg4oh4te9vysxsHGnEmUVE7IiIjfn5f0mj/tOB+cDyvNlyUudARLwTES+QlkNsVThFvg9vwcxs\nXGtEZ9FK0jeA75LWv50WaRF0SNPb2+YuobspRMzMLGtUZ5HT7T4M3DCUMGtInmlY/zUzM7MJqDGd\nRU7U9jCwIiKG8uf8J49nDI1rvFNRzYhSiJiZWWca0Vnkqev3AVsj4jctL60EFuTnC4DHK6raO0Ve\n0hdIU+RXdru9ZmYTTVPuhjqdNJFkCzCUf+dW0rjFg8DXSSkXLoiI9yQdDbxIysPzKfABMCsidkma\nB/yW4ZQNd/T1zZiZjUON6CzMzKzZGnEZyszMms2dhZmZVXJnYWZmldxZmJlZJXcWZmZWyZ2FmZlV\ncmdhZmaV3FmYmVmlzwAAPWE0pa8fbgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KRNbTY0BTQWK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}