proyecto_forma_de_onda_2019.ipynb

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    "colab": {
      "name": "proyecto_forma_de_onda_2019.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anadiedrichs/time-series-analysis/blob/master/proyecto_forma_de_onda_2019.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krEUB4x2U-CL",
        "colab_type": "text"
      },
      "source": [
        "# Laboratorio #1 Proyecto forma de onda - Unidad análisis de series temporales - Teleinformática IoT - 2019\n",
        "\n",
        "**Docente: Ing. Ana Laura Diedrichs**\n",
        "\n",
        "Consultas: lunes 19 hs en sala consulta de sistemas.\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/zUnbfN79YEEeJFz7A) 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",
        " \n",
        " Fecha límite de entrega: 17 de octubre de 2019."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFZazCdSJiQI",
        "colab_type": "text"
      },
      "source": [
        "## Intro\n",
        "\n",
        "Durante el año 2018 era la primera vez que se dictaba esta unidad en la materia. En la primer clase realicé la pregunta sobre si les interesaba algunos datos en particular para analizar o algún dataset. Un alumno nos acercó su inquietud y compartió el dataset que es el que vamos a usar en este laboratorio.\n",
        "\n",
        "El dataset contiene mediciones de una toma de corriente trifásica.\n",
        "\n",
        "Vea más sobre corrienta alterna en https://es.wikipedia.org/wiki/Corriente_alterna\n",
        "\n",
        "Notará que las señales son senoidales.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qzXxPXaoKuVk",
        "colab_type": "text"
      },
      "source": [
        "## Carga del dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j72fwhSrUvM1",
        "colab_type": "text"
      },
      "source": [
        "### por pasos: chusmeando con la CLI primero, luego importar con pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEcilBm1Kuv2",
        "colab_type": "code",
        "outputId": "0eb5b68a-5383-4cd5-e72f-3e1d3ecc8ab8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!wget  https://github.com/anadiedrichs/time-series-analysis/raw/master/datasets/forma_de_onda/forma_de_onda.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-10 16:03:40--  https://github.com/anadiedrichs/time-series-analysis/raw/master/datasets/forma_de_onda/forma_de_onda.csv\n",
            "Resolving github.com (github.com)... 140.82.114.4\n",
            "Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/anadiedrichs/time-series-analysis/master/datasets/forma_de_onda/forma_de_onda.csv [following]\n",
            "--2019-10-10 16:03:40--  https://raw.githubusercontent.com/anadiedrichs/time-series-analysis/master/datasets/forma_de_onda/forma_de_onda.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 48250410 (46M) [text/plain]\n",
            "Saving to: ‘forma_de_onda.csv.1’\n",
            "\n",
            "forma_de_onda.csv.1 100%[===================>]  46.01M   166MB/s    in 0.3s    \n",
            "\n",
            "2019-10-10 16:03:41 (166 MB/s) - ‘forma_de_onda.csv.1’ saved [48250410/48250410]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPoAcyQpOf5a",
        "colab_type": "code",
        "outputId": "d20029d6-9c06-4388-a680-4545fbe27f50",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls -lh"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 140M\n",
            "-rw-r--r-- 1 root root  47M Oct 10 15:17 forma_de_onda.csv\n",
            "-rw-r--r-- 1 root root  47M Oct 10 16:03 forma_de_onda.csv.1\n",
            "-rw-r--r-- 1 root root  48M Oct 10 15:11 forma_de_onda.txt\n",
            "drwxr-xr-x 1 root root 4.0K Aug 27 16:17 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "diASofnZU8A4",
        "colab_type": "text"
      },
      "source": [
        "Notamos que forma_de_onda.csv pesa unos 47 Megabytes.\n",
        "Chusmeo las primeras líneas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAyVSMn2OiW8",
        "colab_type": "code",
        "outputId": "11f681d5-d1a0-4c30-97b1-16d68887bfa8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!head forma_de_onda.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\"Fecha\",\"Hora\",\"AN(V)\",\"A(A)\",\"BN(V)\",\"B(A)\",\"CN(V)\",\"C(A)\",\"NG(V)\",\"N(A)\"\n",
            "09/20/18,\"18:38:36.120\",313.6751,-154.1138,-66.1186,78.2013,-240.5648,64.4684,0,42.3431\n",
            "09/20/18,\"18:38:36.120\",311.6035,-144.1956,-42.252,71.3348,-258.9934,68.6646,-0.0863,28.2288\n",
            "09/20/18,\"18:38:36.121\",305.216,-128.1738,-16.2275,64.0869,-275.9115,70.5719,-0.2158,6.8665\n",
            "09/20/18,\"18:38:36.121\",291.7506,-111.7706,8.5885,58.7463,-289.3769,72.4792,-0.2158,-17.5476\n",
            "09/20/18,\"18:38:36.121\",273.8831,-102.9968,31.8509,54.5502,-299.6054,79.3457,-0.1295,-39.2914\n",
            "09/20/18,\"18:38:36.121\",254.6344,-99.9451,54.8111,47.3022,-305.6908,90.7898,-0.0432,-51.1169\n",
            "09/20/18,\"18:38:36.122\",235.4721,-98.8007,79.3682,36.2396,-309.1434,106.8115,-0.0863,-57.9834\n",
            "09/20/18,\"18:38:36.122\",215.0582,-94.986,105.6085,23.6511,-311.4308,122.8333,-0.259,-68.6646\n",
            "09/20/18,\"18:38:36.122\",191.4074,-85.8307,132.3236,12.97,-312.9414,134.6588,-0.3884,-85.8307\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZdA-AEqdVNhj",
        "colab_type": "text"
      },
      "source": [
        "Observe como están separados los campos de información (por coma), el formato de la fecha y la hora.\n",
        "\n",
        "\n",
        "Importamos usando la funcion read_csv, puede demorar un poco en ejecutarse."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8-DQKasOnV-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from pandas import Series\n",
        "data = pd.read_csv('forma_de_onda.csv',header=0, parse_dates=[0],squeeze=True) #index_col=0,\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pu7tTTJXSiJn",
        "colab_type": "text"
      },
      "source": [
        "index_col es 0 : toma la primer columna como indice por defecto \n",
        "\n",
        "\n",
        "header es 0, la primer fila es usada como cabecera\n",
        "\n",
        "\n",
        "parse_dates es True, intenta convertir los tipos de datos a tipo DateTime\n",
        "\n",
        "La option squeeze=True regresa tipo Series"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmoGcUZmTj2T",
        "colab_type": "text"
      },
      "source": [
        "Mostramos las primeras 10 líneas del dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQX3eIwRRr5N",
        "colab_type": "code",
        "outputId": "9f96c1a9-01fe-4d18-9be1-d345199fa05b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data.head(10) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fecha</th>\n",
              "      <th>Hora</th>\n",
              "      <th>AN(V)</th>\n",
              "      <th>A(A)</th>\n",
              "      <th>BN(V)</th>\n",
              "      <th>B(A)</th>\n",
              "      <th>CN(V)</th>\n",
              "      <th>C(A)</th>\n",
              "      <th>NG(V)</th>\n",
              "      <th>N(A)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.120</td>\n",
              "      <td>313.6751</td>\n",
              "      <td>-154.1138</td>\n",
              "      <td>-66.1186</td>\n",
              "      <td>78.2013</td>\n",
              "      <td>-240.5648</td>\n",
              "      <td>64.4684</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>42.3431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.120</td>\n",
              "      <td>311.6035</td>\n",
              "      <td>-144.1956</td>\n",
              "      <td>-42.2520</td>\n",
              "      <td>71.3348</td>\n",
              "      <td>-258.9934</td>\n",
              "      <td>68.6646</td>\n",
              "      <td>-0.0863</td>\n",
              "      <td>28.2288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.121</td>\n",
              "      <td>305.2160</td>\n",
              "      <td>-128.1738</td>\n",
              "      <td>-16.2275</td>\n",
              "      <td>64.0869</td>\n",
              "      <td>-275.9115</td>\n",
              "      <td>70.5719</td>\n",
              "      <td>-0.2158</td>\n",
              "      <td>6.8665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.121</td>\n",
              "      <td>291.7506</td>\n",
              "      <td>-111.7706</td>\n",
              "      <td>8.5885</td>\n",
              "      <td>58.7463</td>\n",
              "      <td>-289.3769</td>\n",
              "      <td>72.4792</td>\n",
              "      <td>-0.2158</td>\n",
              "      <td>-17.5476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.121</td>\n",
              "      <td>273.8831</td>\n",
              "      <td>-102.9968</td>\n",
              "      <td>31.8509</td>\n",
              "      <td>54.5502</td>\n",
              "      <td>-299.6054</td>\n",
              "      <td>79.3457</td>\n",
              "      <td>-0.1295</td>\n",
              "      <td>-39.2914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.121</td>\n",
              "      <td>254.6344</td>\n",
              "      <td>-99.9451</td>\n",
              "      <td>54.8111</td>\n",
              "      <td>47.3022</td>\n",
              "      <td>-305.6908</td>\n",
              "      <td>90.7898</td>\n",
              "      <td>-0.0432</td>\n",
              "      <td>-51.1169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.122</td>\n",
              "      <td>235.4721</td>\n",
              "      <td>-98.8007</td>\n",
              "      <td>79.3682</td>\n",
              "      <td>36.2396</td>\n",
              "      <td>-309.1434</td>\n",
              "      <td>106.8115</td>\n",
              "      <td>-0.0863</td>\n",
              "      <td>-57.9834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.122</td>\n",
              "      <td>215.0582</td>\n",
              "      <td>-94.9860</td>\n",
              "      <td>105.6085</td>\n",
              "      <td>23.6511</td>\n",
              "      <td>-311.4308</td>\n",
              "      <td>122.8333</td>\n",
              "      <td>-0.2590</td>\n",
              "      <td>-68.6646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.122</td>\n",
              "      <td>191.4074</td>\n",
              "      <td>-85.8307</td>\n",
              "      <td>132.3236</td>\n",
              "      <td>12.9700</td>\n",
              "      <td>-312.9414</td>\n",
              "      <td>134.6588</td>\n",
              "      <td>-0.3884</td>\n",
              "      <td>-85.8307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.122</td>\n",
              "      <td>164.5629</td>\n",
              "      <td>-73.6237</td>\n",
              "      <td>157.8733</td>\n",
              "      <td>4.1962</td>\n",
              "      <td>-313.9340</td>\n",
              "      <td>141.1438</td>\n",
              "      <td>-0.3453</td>\n",
              "      <td>-104.1412</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "       Fecha          Hora     AN(V)  ...      C(A)   NG(V)      N(A)\n",
              "0 2018-09-20  18:38:36.120  313.6751  ...   64.4684  0.0000   42.3431\n",
              "1 2018-09-20  18:38:36.120  311.6035  ...   68.6646 -0.0863   28.2288\n",
              "2 2018-09-20  18:38:36.121  305.2160  ...   70.5719 -0.2158    6.8665\n",
              "3 2018-09-20  18:38:36.121  291.7506  ...   72.4792 -0.2158  -17.5476\n",
              "4 2018-09-20  18:38:36.121  273.8831  ...   79.3457 -0.1295  -39.2914\n",
              "5 2018-09-20  18:38:36.121  254.6344  ...   90.7898 -0.0432  -51.1169\n",
              "6 2018-09-20  18:38:36.122  235.4721  ...  106.8115 -0.0863  -57.9834\n",
              "7 2018-09-20  18:38:36.122  215.0582  ...  122.8333 -0.2590  -68.6646\n",
              "8 2018-09-20  18:38:36.122  191.4074  ...  134.6588 -0.3884  -85.8307\n",
              "9 2018-09-20  18:38:36.122  164.5629  ...  141.1438 -0.3453 -104.1412\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H12BREmNQfLq",
        "colab_type": "code",
        "outputId": "1ac2c111-81a3-4bd4-909a-62da480f7392",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "type(data) #el tipo de variable que es data, es un DataFrame"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33CBohDDVvND",
        "colab_type": "text"
      },
      "source": [
        "## Accediendo a pedacitos del dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIx_EZstWBlR",
        "colab_type": "text"
      },
      "source": [
        "Veo desde la fila 1 a la 20, las columnas 2 a la 7"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ENTzw4KV036",
        "colab_type": "code",
        "outputId": "a58b365c-cb29-488c-8aa3-83a541ff3df5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "source": [
        "data.iloc[1:20,2:7]"
      ],
      "execution_count": 0,
      "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>AN(V)</th>\n",
              "      <th>A(A)</th>\n",
              "      <th>BN(V)</th>\n",
              "      <th>B(A)</th>\n",
              "      <th>CN(V)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>311.6035</td>\n",
              "      <td>-144.1956</td>\n",
              "      <td>-42.2520</td>\n",
              "      <td>71.3348</td>\n",
              "      <td>-258.9934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>305.2160</td>\n",
              "      <td>-128.1738</td>\n",
              "      <td>-16.2275</td>\n",
              "      <td>64.0869</td>\n",
              "      <td>-275.9115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>291.7506</td>\n",
              "      <td>-111.7706</td>\n",
              "      <td>8.5885</td>\n",
              "      <td>58.7463</td>\n",
              "      <td>-289.3769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>273.8831</td>\n",
              "      <td>-102.9968</td>\n",
              "      <td>31.8509</td>\n",
              "      <td>54.5502</td>\n",
              "      <td>-299.6054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>254.6344</td>\n",
              "      <td>-99.9451</td>\n",
              "      <td>54.8111</td>\n",
              "      <td>47.3022</td>\n",
              "      <td>-305.6908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>235.4721</td>\n",
              "      <td>-98.8007</td>\n",
              "      <td>79.3682</td>\n",
              "      <td>36.2396</td>\n",
              "      <td>-309.1434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>215.0582</td>\n",
              "      <td>-94.9860</td>\n",
              "      <td>105.6085</td>\n",
              "      <td>23.6511</td>\n",
              "      <td>-311.4308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>191.4074</td>\n",
              "      <td>-85.8307</td>\n",
              "      <td>132.3236</td>\n",
              "      <td>12.9700</td>\n",
              "      <td>-312.9414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>164.5629</td>\n",
              "      <td>-73.6237</td>\n",
              "      <td>157.8733</td>\n",
              "      <td>4.1962</td>\n",
              "      <td>-313.9340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>136.0784</td>\n",
              "      <td>-61.4166</td>\n",
              "      <td>181.3947</td>\n",
              "      <td>-3.4332</td>\n",
              "      <td>-314.3656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>107.7233</td>\n",
              "      <td>-51.1169</td>\n",
              "      <td>203.4054</td>\n",
              "      <td>-14.1144</td>\n",
              "      <td>-314.3224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>82.7346</td>\n",
              "      <td>-42.7246</td>\n",
              "      <td>223.3877</td>\n",
              "      <td>-27.0844</td>\n",
              "      <td>-314.0203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>55.4154</td>\n",
              "      <td>-33.5693</td>\n",
              "      <td>245.0533</td>\n",
              "      <td>-43.1061</td>\n",
              "      <td>-313.1140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>28.5277</td>\n",
              "      <td>-25.1770</td>\n",
              "      <td>263.9998</td>\n",
              "      <td>-57.9834</td>\n",
              "      <td>-310.3087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2.6327</td>\n",
              "      <td>-17.5476</td>\n",
              "      <td>280.7452</td>\n",
              "      <td>-70.9534</td>\n",
              "      <td>-300.2528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>-21.2771</td>\n",
              "      <td>-12.2070</td>\n",
              "      <td>294.8148</td>\n",
              "      <td>-82.0160</td>\n",
              "      <td>-282.4716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>-43.7194</td>\n",
              "      <td>-6.4850</td>\n",
              "      <td>302.0223</td>\n",
              "      <td>-92.3157</td>\n",
              "      <td>-262.4461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>-67.3271</td>\n",
              "      <td>3.0518</td>\n",
              "      <td>305.5613</td>\n",
              "      <td>-107.5745</td>\n",
              "      <td>-243.7585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>-93.4810</td>\n",
              "      <td>17.1661</td>\n",
              "      <td>308.3234</td>\n",
              "      <td>-127.4109</td>\n",
              "      <td>-225.4162</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       AN(V)      A(A)     BN(V)      B(A)     CN(V)\n",
              "1   311.6035 -144.1956  -42.2520   71.3348 -258.9934\n",
              "2   305.2160 -128.1738  -16.2275   64.0869 -275.9115\n",
              "3   291.7506 -111.7706    8.5885   58.7463 -289.3769\n",
              "4   273.8831 -102.9968   31.8509   54.5502 -299.6054\n",
              "5   254.6344  -99.9451   54.8111   47.3022 -305.6908\n",
              "6   235.4721  -98.8007   79.3682   36.2396 -309.1434\n",
              "7   215.0582  -94.9860  105.6085   23.6511 -311.4308\n",
              "8   191.4074  -85.8307  132.3236   12.9700 -312.9414\n",
              "9   164.5629  -73.6237  157.8733    4.1962 -313.9340\n",
              "10  136.0784  -61.4166  181.3947   -3.4332 -314.3656\n",
              "11  107.7233  -51.1169  203.4054  -14.1144 -314.3224\n",
              "12   82.7346  -42.7246  223.3877  -27.0844 -314.0203\n",
              "13   55.4154  -33.5693  245.0533  -43.1061 -313.1140\n",
              "14   28.5277  -25.1770  263.9998  -57.9834 -310.3087\n",
              "15    2.6327  -17.5476  280.7452  -70.9534 -300.2528\n",
              "16  -21.2771  -12.2070  294.8148  -82.0160 -282.4716\n",
              "17  -43.7194   -6.4850  302.0223  -92.3157 -262.4461\n",
              "18  -67.3271    3.0518  305.5613 -107.5745 -243.7585\n",
              "19  -93.4810   17.1661  308.3234 -127.4109 -225.4162"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPyub-geWI4Y",
        "colab_type": "text"
      },
      "source": [
        "Grafico un rango del dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iADLliEaT-EU",
        "colab_type": "code",
        "outputId": "bc5703b6-96e8-40a9-f9c2-2a67d0e77fed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "data.iloc[1:100,2:7].plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f12c65ce0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rj96o54NfPyDCLYIJoROaWcL2ycPRD1OoLWT9xfX1am8yCY5tScEz0JGg6Por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\nmhbhdCLQNE2LcP8PHoHjWYvkg5QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3P5LZeYWW4o",
        "colab_type": "text"
      },
      "source": [
        "## Propiedades del dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LaXD1BTWegq",
        "colab_type": "text"
      },
      "source": [
        "Cuántas filas y columnas tiene"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lO2dbCkPWakd",
        "colab_type": "code",
        "outputId": "70707182-e6f1-46f4-e8b5-3327a965603e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(524088, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pc-ZK5hoWhMX",
        "colab_type": "text"
      },
      "source": [
        "Son las mismas columnas que miramos ejecutando *head*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qa9R6A85Wjhr",
        "colab_type": "code",
        "outputId": "3ea4f976-6524-4dc7-ca3e-1160425a6205",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "data.head(10)"
      ],
      "execution_count": 0,
      "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>Fecha</th>\n",
              "      <th>Hora</th>\n",
              "      <th>AN(V)</th>\n",
              "      <th>A(A)</th>\n",
              "      <th>BN(V)</th>\n",
              "      <th>B(A)</th>\n",
              "      <th>CN(V)</th>\n",
              "      <th>C(A)</th>\n",
              "      <th>NG(V)</th>\n",
              "      <th>N(A)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.120</td>\n",
              "      <td>313.6751</td>\n",
              "      <td>-154.1138</td>\n",
              "      <td>-66.1186</td>\n",
              "      <td>78.2013</td>\n",
              "      <td>-240.5648</td>\n",
              "      <td>64.4684</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>42.3431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.120</td>\n",
              "      <td>311.6035</td>\n",
              "      <td>-144.1956</td>\n",
              "      <td>-42.2520</td>\n",
              "      <td>71.3348</td>\n",
              "      <td>-258.9934</td>\n",
              "      <td>68.6646</td>\n",
              "      <td>-0.0863</td>\n",
              "      <td>28.2288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.121</td>\n",
              "      <td>305.2160</td>\n",
              "      <td>-128.1738</td>\n",
              "      <td>-16.2275</td>\n",
              "      <td>64.0869</td>\n",
              "      <td>-275.9115</td>\n",
              "      <td>70.5719</td>\n",
              "      <td>-0.2158</td>\n",
              "      <td>6.8665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.121</td>\n",
              "      <td>291.7506</td>\n",
              "      <td>-111.7706</td>\n",
              "      <td>8.5885</td>\n",
              "      <td>58.7463</td>\n",
              "      <td>-289.3769</td>\n",
              "      <td>72.4792</td>\n",
              "      <td>-0.2158</td>\n",
              "      <td>-17.5476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.121</td>\n",
              "      <td>273.8831</td>\n",
              "      <td>-102.9968</td>\n",
              "      <td>31.8509</td>\n",
              "      <td>54.5502</td>\n",
              "      <td>-299.6054</td>\n",
              "      <td>79.3457</td>\n",
              "      <td>-0.1295</td>\n",
              "      <td>-39.2914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.121</td>\n",
              "      <td>254.6344</td>\n",
              "      <td>-99.9451</td>\n",
              "      <td>54.8111</td>\n",
              "      <td>47.3022</td>\n",
              "      <td>-305.6908</td>\n",
              "      <td>90.7898</td>\n",
              "      <td>-0.0432</td>\n",
              "      <td>-51.1169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.122</td>\n",
              "      <td>235.4721</td>\n",
              "      <td>-98.8007</td>\n",
              "      <td>79.3682</td>\n",
              "      <td>36.2396</td>\n",
              "      <td>-309.1434</td>\n",
              "      <td>106.8115</td>\n",
              "      <td>-0.0863</td>\n",
              "      <td>-57.9834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.122</td>\n",
              "      <td>215.0582</td>\n",
              "      <td>-94.9860</td>\n",
              "      <td>105.6085</td>\n",
              "      <td>23.6511</td>\n",
              "      <td>-311.4308</td>\n",
              "      <td>122.8333</td>\n",
              "      <td>-0.2590</td>\n",
              "      <td>-68.6646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.122</td>\n",
              "      <td>191.4074</td>\n",
              "      <td>-85.8307</td>\n",
              "      <td>132.3236</td>\n",
              "      <td>12.9700</td>\n",
              "      <td>-312.9414</td>\n",
              "      <td>134.6588</td>\n",
              "      <td>-0.3884</td>\n",
              "      <td>-85.8307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.122</td>\n",
              "      <td>164.5629</td>\n",
              "      <td>-73.6237</td>\n",
              "      <td>157.8733</td>\n",
              "      <td>4.1962</td>\n",
              "      <td>-313.9340</td>\n",
              "      <td>141.1438</td>\n",
              "      <td>-0.3453</td>\n",
              "      <td>-104.1412</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Fecha          Hora     AN(V)  ...      C(A)   NG(V)      N(A)\n",
              "0 2018-09-20  18:38:36.120  313.6751  ...   64.4684  0.0000   42.3431\n",
              "1 2018-09-20  18:38:36.120  311.6035  ...   68.6646 -0.0863   28.2288\n",
              "2 2018-09-20  18:38:36.121  305.2160  ...   70.5719 -0.2158    6.8665\n",
              "3 2018-09-20  18:38:36.121  291.7506  ...   72.4792 -0.2158  -17.5476\n",
              "4 2018-09-20  18:38:36.121  273.8831  ...   79.3457 -0.1295  -39.2914\n",
              "5 2018-09-20  18:38:36.121  254.6344  ...   90.7898 -0.0432  -51.1169\n",
              "6 2018-09-20  18:38:36.122  235.4721  ...  106.8115 -0.0863  -57.9834\n",
              "7 2018-09-20  18:38:36.122  215.0582  ...  122.8333 -0.2590  -68.6646\n",
              "8 2018-09-20  18:38:36.122  191.4074  ...  134.6588 -0.3884  -85.8307\n",
              "9 2018-09-20  18:38:36.122  164.5629  ...  141.1438 -0.3453 -104.1412\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q44t7yS5WzAC",
        "colab_type": "text"
      },
      "source": [
        "¿Lo siguiente muestra número de filas o columnas?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLw18UmQhcxT",
        "colab_type": "code",
        "outputId": "3f69be6e-6367-468e-e7d9-c888fca2b971",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data.shape[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "524088"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGMKg-Z4W4TM",
        "colab_type": "text"
      },
      "source": [
        "¿Lo siguiente muestra número de filas o columnas?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yB0pe-vYhgOb",
        "colab_type": "code",
        "outputId": "2fae7733-3be8-4069-aa3f-43bd0beb44ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data.shape[1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21mqR70NXHVN",
        "colab_type": "text"
      },
      "source": [
        "Podemos observar de cada DataFrame algunas características estadísticas usando *describe()*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7UKM55WXDiQ",
        "colab_type": "code",
        "outputId": "19489c7b-6144-4490-bc0c-d27ea07c3814",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 0,
      "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>AN(V)</th>\n",
              "      <th>A(A)</th>\n",
              "      <th>BN(V)</th>\n",
              "      <th>B(A)</th>\n",
              "      <th>CN(V)</th>\n",
              "      <th>C(A)</th>\n",
              "      <th>NG(V)</th>\n",
              "      <th>N(A)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>517241.000000</td>\n",
              "      <td>517241.000000</td>\n",
              "      <td>517241.000000</td>\n",
              "      <td>517241.000000</td>\n",
              "      <td>517241.000000</td>\n",
              "      <td>517241.000000</td>\n",
              "      <td>517241.000000</td>\n",
              "      <td>517241.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.041821</td>\n",
              "      <td>0.086277</td>\n",
              "      <td>0.076139</td>\n",
              "      <td>0.111673</td>\n",
              "      <td>0.055757</td>\n",
              "      <td>0.038909</td>\n",
              "      <td>-0.027649</td>\n",
              "      <td>0.021303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>230.014488</td>\n",
              "      <td>102.165137</td>\n",
              "      <td>228.445619</td>\n",
              "      <td>114.830872</td>\n",
              "      <td>230.294228</td>\n",
              "      <td>95.693325</td>\n",
              "      <td>0.332933</td>\n",
              "      <td>67.514958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-315.617200</td>\n",
              "      <td>-248.336800</td>\n",
              "      <td>-313.847700</td>\n",
              "      <td>-211.334200</td>\n",
              "      <td>-315.228800</td>\n",
              "      <td>-188.446000</td>\n",
              "      <td>-1.165300</td>\n",
              "      <td>-141.143800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-234.047900</td>\n",
              "      <td>-91.552700</td>\n",
              "      <td>-231.717300</td>\n",
              "      <td>-102.615400</td>\n",
              "      <td>-234.565800</td>\n",
              "      <td>-82.016000</td>\n",
              "      <td>-0.259000</td>\n",
              "      <td>-49.972500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.086300</td>\n",
              "      <td>-0.381500</td>\n",
              "      <td>0.302100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.388400</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>234.393100</td>\n",
              "      <td>91.934200</td>\n",
              "      <td>232.192100</td>\n",
              "      <td>102.615400</td>\n",
              "      <td>234.695200</td>\n",
              "      <td>82.397500</td>\n",
              "      <td>0.215800</td>\n",
              "      <td>49.972500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>315.703500</td>\n",
              "      <td>250.625600</td>\n",
              "      <td>313.977200</td>\n",
              "      <td>212.860100</td>\n",
              "      <td>315.401400</td>\n",
              "      <td>187.301600</td>\n",
              "      <td>0.820000</td>\n",
              "      <td>139.999400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               AN(V)           A(A)  ...          NG(V)           N(A)\n",
              "count  517241.000000  517241.000000  ...  517241.000000  517241.000000\n",
              "mean        0.041821       0.086277  ...      -0.027649       0.021303\n",
              "std       230.014488     102.165137  ...       0.332933      67.514958\n",
              "min      -315.617200    -248.336800  ...      -1.165300    -141.143800\n",
              "25%      -234.047900     -91.552700  ...      -0.259000     -49.972500\n",
              "50%         0.086300      -0.381500  ...       0.000000       0.000000\n",
              "75%       234.393100      91.934200  ...       0.215800      49.972500\n",
              "max       315.703500     250.625600  ...       0.820000     139.999400\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FaJ6Kw4fXXeo",
        "colab_type": "text"
      },
      "source": [
        "## Valores perdidos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ziS50Ji-XZlA",
        "colab_type": "text"
      },
      "source": [
        "Chusmeamos el dataset nuevamente"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCcW7Tjuiddc",
        "colab_type": "code",
        "outputId": "9f6d7d98-bd43-472e-bba6-68c9b36a90ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "data.iloc[50:60,0:5]"
      ],
      "execution_count": 0,
      "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>Fecha</th>\n",
              "      <th>Hora</th>\n",
              "      <th>AN(V)</th>\n",
              "      <th>A(A)</th>\n",
              "      <th>BN(V)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.133</td>\n",
              "      <td>-66.8955</td>\n",
              "      <td>38.1470</td>\n",
              "      <td>-236.2921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.134</td>\n",
              "      <td>-42.3384</td>\n",
              "      <td>30.1361</td>\n",
              "      <td>-254.5049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.134</td>\n",
              "      <td>-15.9686</td>\n",
              "      <td>22.1252</td>\n",
              "      <td>-271.9841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.134</td>\n",
              "      <td>9.3222</td>\n",
              "      <td>14.8773</td>\n",
              "      <td>-287.6937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.134</td>\n",
              "      <td>32.6277</td>\n",
              "      <td>10.2997</td>\n",
              "      <td>-298.7423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.135</td>\n",
              "      <td>55.2427</td>\n",
              "      <td>3.8147</td>\n",
              "      <td>-303.7055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.135</td>\n",
              "      <td>80.4472</td>\n",
              "      <td>-8.0109</td>\n",
              "      <td>-306.7266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.135</td>\n",
              "      <td>107.2054</td>\n",
              "      <td>-21.7438</td>\n",
              "      <td>-309.2298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>18:38:36.135</td>\n",
              "      <td>133.6615</td>\n",
              "      <td>-33.9508</td>\n",
              "      <td>-311.0856</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Fecha          Hora     AN(V)     A(A)     BN(V)\n",
              "50 2018-09-20  18:38:36.133  -66.8955  38.1470 -236.2921\n",
              "51 2018-09-20  18:38:36.134  -42.3384  30.1361 -254.5049\n",
              "52 2018-09-20  18:38:36.134  -15.9686  22.1252 -271.9841\n",
              "53        NaT           NaN       NaN      NaN       NaN\n",
              "54 2018-09-20  18:38:36.134    9.3222  14.8773 -287.6937\n",
              "55 2018-09-20  18:38:36.134   32.6277  10.2997 -298.7423\n",
              "56 2018-09-20  18:38:36.135   55.2427   3.8147 -303.7055\n",
              "57 2018-09-20  18:38:36.135   80.4472  -8.0109 -306.7266\n",
              "58 2018-09-20  18:38:36.135  107.2054 -21.7438 -309.2298\n",
              "59 2018-09-20  18:38:36.135  133.6615 -33.9508 -311.0856"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KGtDHcokmnVk",
        "colab_type": "code",
        "outputId": "52c84c88-002f-4315-b485-9eaf3c3cc330",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "data.iloc[1:100,2:7].plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f12c7099da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rj96o54NfPyDCLYIJoROaWcL2ycPRD1OoLWT9xfX1am8yCY5tScEz0JGg6Por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\nmhbhdCLQNE2LcP8PHoHjWYvkg5QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56wEDLyPXlIW",
        "colab_type": "text"
      },
      "source": [
        "### [Actividad]\n",
        "\n",
        "Habrá notado que hay valores pedidos en la serie. \n",
        "\n",
        "Determine si reconstruye la señal compleando los valores perdidos e indicados como NaN o los ignora. Justifique"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "92sQc0UuZkaJ",
        "colab_type": "text"
      },
      "source": [
        "SU RESPUESTA AQUI"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lam1Sg9yZjkB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drZiEfs1YeHI",
        "colab_type": "text"
      },
      "source": [
        "## [actividad] Intervalos de muestreo\n",
        "\n",
        "¿Cada cuánto tenemos una medición? ¿Qué nos puede decir sobre el intervalo de muestreo?\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GAW8EHl5Y-Fi",
        "colab_type": "text"
      },
      "source": [
        "**SU RESPUESTA AQUI**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRMeb0QBkTrx",
        "colab_type": "text"
      },
      "source": [
        "## [actividad] Análisis de la señal\n",
        "\n",
        "¿Son todas las ondas \"perfectamente\" senoidales?\n",
        "\n",
        "¿Por qué cree que alguna no?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZDpfBPr6YtZT",
        "colab_type": "text"
      },
      "source": [
        "**SU RESPUESTA AQUI**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5IeWpd0cXNd",
        "colab_type": "text"
      },
      "source": [
        "### [actividad] Calcule y grafique la FFT de la señal "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvaG7Ij_c1Oe",
        "colab_type": "text"
      },
      "source": [
        "ESCRIBA SUS COMENTARIOS AQUI"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26QM2_DucWG_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LIILrXOwcjtq",
        "colab_type": "text"
      },
      "source": [
        "¿qué concluye al ver este gráfico?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfecfOeRc40r",
        "colab_type": "text"
      },
      "source": [
        "SU RESPUESTA AQUI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhE0XlSNZNs1",
        "colab_type": "text"
      },
      "source": [
        "## [actividad] Calcule la integral de la señal\n",
        "\n",
        "¿Qué significado tiene calcular la integral de un tramo de la señal? Comente con sus colegas, discuta o busque información.\n",
        "\n"
      ]
    }
  ]
}