{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BxTo3qcLlfvz"
      },
      "source": [
        "<h1 align=\"center\"> Auxiliar 1\n",
        "<h2 align=\"center\">  Profesor: Richard Weber\n",
        "<h2 align=\"center\">  Auxiliares: Cristóbal Pérez y Joaquín Roa\n",
        "<h3 align=\"center\"> IN6531-1 - Introducción a la Minería de Datos\n",
        "<h3 align=\"center\"> Primavera 2023\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4M0vicz6l1cz"
      },
      "source": [
        "##¿Por qué utilizar Python?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hp_avNJnkveD"
      },
      "source": [
        "![Python.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PscZ4Jbsl8kR"
      },
      "source": [
        "\n",
        "\n",
        "*   Fácil de usar\n",
        "*   Tiene muchas librerías pre-instaladas\n",
        "*   Permite un alto nivel de abstracción\n",
        "*   Lenguaje más utilizado en Data Science\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_chicENnC4q"
      },
      "source": [
        "##¿Cuántos datos procesaremos?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "npv9EZIJnooM"
      },
      "source": [
        "[¿Por qué usar GPU?](https://www.youtube.com/watch?v=C_wSHKG8_fg) --> Suscribanse al canal"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtABZC2knXrj"
      },
      "source": [
        "**Ruta:** Edit -> Ajusted del notebook -> seleccionar GPU"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ufIMzthmzUr"
      },
      "source": [
        "##Uso de test estadísticos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61agcf6EoX4c"
      },
      "source": [
        "No es el foco principal del curso, sin embargo para saber del tema, veamos las biblias del aprendizaje:\n",
        "\n",
        "*   [Medium](https://medium.com/swlh/what-is-chi-square-test-how-does-it-work-3b7f22c03b01)\n",
        "*   [Towardsdatascience ](https://towardsdatascience.com/chi-squared-test-for-feature-selection-with-implementation-in-python-65b4ae7696db)\n",
        "*   [Stack Overflow](https://stackoverflow.com/questions/29866961/pearsons-chi-square-test-python)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fo94Rz6Cpg0f"
      },
      "source": [
        "##Basics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jPa0PHW3qNIy"
      },
      "source": [
        "###Shortcuts\n",
        "\n",
        "| Acción                                |    Comando    |\n",
        "|---------------------------------------|:-------------:|\n",
        "| Convertir a celda de código (J)       |      `Y`      |\n",
        "| Convertir a celda de texto  (J)       |      `M`      |\n",
        "| Insertar celda de código arriba       |      `A`      |\n",
        "| Insertar celda de código abajo        |      `B`      |\n",
        "| Borrar celda (J)                      |      `DD`     |\n",
        "| Deshacer acción con celdas            |      `Z`      |\n",
        "| Ejecutar Celda                        |  `Ctrl+Enter` |\n",
        "| Ejecutar Celda y pasar a la siguiente | `Shift+Enter` |\n",
        "| Guardar (J)                           |      `S`      |\n",
        "| Interrumpir (J)                       |      `II`     |\n",
        "\n",
        "Para más shorcuts `Tools->Keyboard Shorcuts`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAMyplO9x2rT"
      },
      "source": [
        "###Markdown\n",
        "\n",
        "Es un [lenguaje de marcado](https://es.wikipedia.org/wiki/Lenguaje_de_marcado) que permite escribir de forma programática texto con formato, imágenes, tablas, código, entre otros.\n",
        "\n",
        "[Guia completa de información](https://docs.github.com/es/github/writing-on-github/basic-writing-and-formatting-syntax)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EXGLb29prhcj"
      },
      "source": [
        "##1.Estructuras de datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aB37YIyTrohi"
      },
      "source": [
        "####1.1 Números"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KEsBN7wcr2WS"
      },
      "source": [
        "Python tiene 7 operadores aritméticos básicos\n",
        "\n",
        "| Operador   | Name |\n",
        "|--------------|----------------|\n",
        "| ``+``    | Suma       |\n",
        "| ``-``    | Resta    |                       \n",
        "| ``*``    | Multiplicación |\n",
        "| ``/``    | División  |\n",
        "| ``//``   | División entera|\n",
        "| ``%``    | Modulo        |\n",
        "| ``**``   | Potencia |"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmb_neAcrz17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fb97f73-f0b8-479e-8ead-178473cff7a2"
      },
      "source": [
        "#Con el numeral (gato, hashtag o almohadilla (#)) se comenta el código\n",
        "#Suma\n",
        "3+4"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1FDp-tlmsQds",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ebb256a-4713-4784-9bc4-6cf1ff3718b4"
      },
      "source": [
        "#Resta\n",
        "3-4"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Vjr_vYOsQgS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa8685b5-b5c9-449b-e355-a2fba449b45d"
      },
      "source": [
        "#Multiplicación\n",
        "3*4"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6AOB7S6sQi7",
        "outputId": "196522f1-4bb8-4bf3-eb39-290c7f826a48"
      },
      "source": [
        "#Division\n",
        "101/4"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25.25"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vk-4wlPqsQla",
        "outputId": "c21086a3-92d6-4c61-f8b7-68cb7c6f03ad"
      },
      "source": [
        "#Division entera\n",
        "101//4"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82F4qy8rsQoj"
      },
      "source": [
        "#Modulo o resto"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nrpCD0ClsQqZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "501d66f4-0a1e-4a9d-f553-de04ec0d9806"
      },
      "source": [
        "101%4"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "102%3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdZBq-bXIxF6",
        "outputId": "ba120908-4efc-4e6a-aed1-2a78ed3cdd41"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ChAyh_Uswiu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e650f99-6492-462d-c2b7-8a1282a7a85c"
      },
      "source": [
        "#Potencia\n",
        "3**4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "81"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_3rQisCswlR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "845bcf91-02b4-4ff4-9c19-d42b11c45eb9"
      },
      "source": [
        "3**(1/4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.3160740129524924"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bb2bemYRswn4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74ea6af9-bdc1-4124-c9cf-9d18fc0ad482"
      },
      "source": [
        "#¿Que pasa si hago más de una formula en una casilla?\n",
        "print(3+4)\n",
        "print(3*4)\n",
        "print(3//4)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n",
            "12\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "3+4\n",
        "3*4\n",
        "3//4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuwzrctnJAgG",
        "outputId": "542ae39f-a378-4ec8-eec5-c3dfcffa7f5a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Od6TYMiQswqf"
      },
      "source": [
        "#Asignar una variable\n",
        "a = 3+4"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0yU06ehz_MB9",
        "outputId": "edf57b6e-1967-4aa5-d596-051f5ca6aebc"
      },
      "source": [
        "a"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n46AMoRltE-L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16b73790-800e-4cdc-83fb-438dd25b3914"
      },
      "source": [
        "#¿Puedo mutar esa asignación?\n",
        "a = a + 1\n",
        "a"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_NmlCn7tFA6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48c790ab-3f75-42af-d161-7a4eacb38905"
      },
      "source": [
        "a += 1\n",
        "a"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJCbj3DS_c2R",
        "outputId": "e5ccd142-4fb9-4c23-c27a-608f59322f64"
      },
      "source": [
        "a *= 2\n",
        "a"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykgqyaY3tFEG"
      },
      "source": [
        "a = 45"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cOLiXWNfJUdw",
        "outputId": "d20d92ab-a834-44d4-cfbf-c5380c73d02d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "45"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9pq4QOf_m5z"
      },
      "source": [
        "var = 12"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Variables y memoria\n",
        "# a y b hacen referencia al objeto que representa al entero 1\n",
        "a = 1\n",
        "b = 1"
      ],
      "metadata": {
        "id": "RAOikEPyKTcm"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Referencian a la misma dirección de memoria\n",
        "id(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ii3E8a7MKeeW",
        "outputId": "5022584b-89e9-45cd-bed5-88f08083bfcc"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "132651291410672"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SdvrhcseKeZX",
        "outputId": "f0ee183f-b1a1-459d-c0c6-df127f116b8a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "132651291410672"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2JWn7agzKeEk",
        "outputId": "316e1d86-8690-4195-f93c-ad5b5a616f30"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "132651291410672"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# b es asignado con el objeto que representa el entero 2\n",
        "# a y b referencian a diferentes direcciones de memoria\n",
        "# a mantiene la referencia al entero 1\n",
        "b = 2\n",
        "id(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1fZno15KbkY",
        "outputId": "9ebad63c-9375-4347-8784-45eb0adfa8e1"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "132651291410672"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id(b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s3FUJtvxKl1c",
        "outputId": "da41df03-00ab-4a80-bce8-7b3b940d0ab6"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "132651291410704"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# a es asignada con el valor de b\n",
        "# a y b referencian al mismo objeto y, por tanto,\n",
        "# a la misma dirección de memoria\n",
        "a = b"
      ],
      "metadata": {
        "id": "X1LFOXBUKoed"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5osRwA4AKqa-",
        "outputId": "a9f79cc3-0735-4996-e14b-16789f20d958"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "132651291410704"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WppD6N9OKrPA",
        "outputId": "d3dbd495-140b-496c-bf4b-ed64c432f2d4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z4peGRLKrsPr"
      },
      "source": [
        "####1.2 Strings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pEwKpipTteb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7d7a4c98-04c2-415c-e635-ce85e970907c"
      },
      "source": [
        "'Hola IN6531'"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hola IN6531'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DparpDKAte2k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "91afbfae-f703-4110-dc4d-e7b13c5daee5"
      },
      "source": [
        "\"O'higgins\""
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"O'higgins\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "j6hWEcgluIbG",
        "outputId": "747c35e0-e2a5-4c0a-ba35-acd2cc47838b"
      },
      "source": [
        "#¿Puedo asignar un string como una variable?\n",
        "a = 'Hola IN6531'\n",
        "a"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hola IN6531'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "rIu6liCjAcBK",
        "outputId": "b74c45ab-dc32-4b4d-e9a4-da587d6c8fe7"
      },
      "source": [
        "a = 45\n",
        "a = str(a)\n",
        "a"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'45'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I1KdgBXIte9b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eecbd28f-8859-41d4-c5a2-c1e8a52532b3"
      },
      "source": [
        "#Largo del string\n",
        "len(a)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1N14rwclt8Bh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cfdcae38-0621-489e-d434-b8e52a9c6863"
      },
      "source": [
        "#Acceder a elementos\n",
        "#OJO que python parte del indice 0\n",
        "a[0]"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CRPsCw6Dt8FY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "af4d8ea2-4bf2-440d-ec2e-c20c22fa320f"
      },
      "source": [
        "#Primer elemento\n",
        "a[0]"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aTVRc1zit8Id",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "177ddcf3-1f49-4ca1-a54c-f8544367afc1"
      },
      "source": [
        "#Ultimo elemento\n",
        "a[-1]"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'5'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MoJRd3YGE93r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d136d042-e6ff-46f3-b799-b1a26a1d11a1"
      },
      "source": [
        "a[len(a)-1]"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'5'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Elemento fuera de rango\n",
        "a[5]"
      ],
      "metadata": {
        "id": "i8hP-rzR_ZFx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "9fcaa79e-5860-446a-c952-363cea532ed8"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-38-b5aca0e32f32>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Elemento fuera de rango\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m: string index out of range"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1hX75xnpt8KJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e72c913a-12a7-4f9e-c2d1-e4a1001ede2f"
      },
      "source": [
        "#Mostrar todos los elementos\n",
        "a[:]"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'45'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uO3QfETEFGlF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ba091627-346b-443b-9c38-29348ca75700"
      },
      "source": [
        "a"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'45'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqS-phAZt8Ni",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "25c7e694-fa0a-4976-81e9-e8a99acf6845"
      },
      "source": [
        "#Mostrar todos menos el ultimo\n",
        "a[:-1]"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbFWplN0FQK9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "049c0406-27ed-47c4-dde9-b71a3f20a6ef"
      },
      "source": [
        "#Mostrar todos menos el penultimo\n",
        "a[:-2]"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49zDo5mcu3ek",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bf41a628-3d89-43b5-d31a-6e4024bbd08e"
      },
      "source": [
        "#Mostrar desde el segundo caracter\n",
        "a[1:]"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'5'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VV-QPZtRt8Pm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a852e9d0-72e5-4afc-a72c-43e741bef4a6"
      },
      "source": [
        "#Mostrar elementos entre la posicion 2 y 4\n",
        "a[1:4]"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'5'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nq5xEvT1t8So",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "outputId": "49f7ae8c-6ad7-48e2-b806-1e8f62cc7543"
      },
      "source": [
        "a[200]"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-46-a95a8de53d36>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m: string index out of range"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zw95x2H2vLWW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "21026a78-1889-415e-f146-6e63d914d90f"
      },
      "source": [
        "#¿Puedo cambiar un caracter dentro del string?\n",
        "a[8]='2'"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-47-466fbae740c8>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#¿Puedo cambiar un caracter dentro del string?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04ylg5N0FtGN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6b6e3177-e6cb-45ff-a474-e7c1b77ff339"
      },
      "source": [
        "a"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'45'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofV2mp2bvLbA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0622bf7c-d632-4938-bf7e-03f3050ccddb"
      },
      "source": [
        "#concatenar\n",
        "'Hola' + 'IN6531'"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'HolaIN6531'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5qi15Q_vLeL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1fb0c586-817b-48b5-8b06-81a970e8665f"
      },
      "source": [
        "a = 'Hola '\n",
        "b = 'IN6531'\n",
        "a + b"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hola IN6531'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ztxoeAYCvLhz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e83a8ce9-57b3-4aa2-f6cb-315376e1e8a8"
      },
      "source": [
        "c = a + b\n",
        "c"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hola IN6531'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvGEkkMkvLlY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9ac4f5b4-1ec2-4cfd-f4b3-27dbe9a942b7"
      },
      "source": [
        "c = a+' '+b\n",
        "c"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hola  IN6531'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADCkfp_DvLn8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4652cd26-57c8-48a0-c324-a75a31baffc0"
      },
      "source": [
        "#formatear un string\n",
        "'Mi nombre es {n} y soy de {s}'.format(n='Cristóbal',s='Santa Cruz')"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Mi nombre es Cristóbal y soy de Santa Cruz'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5etR1C_vLq9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2b09641d-a3ed-445b-d26b-2df54e46e3db"
      },
      "source": [
        "n = 'Cristóbal'\n",
        "s = 'Santa Cruz'\n",
        "f'Mi nombre es {n} y soy de {s}'"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Mi nombre es Cristóbal y soy de Santa Cruz'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8sws4d4uvLtl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "074403f3-d733-4668-bb1d-e2682ca2fe07"
      },
      "source": [
        "print(c)\n",
        "print('Mi nombre es {n} y soy de {s}'.format(n='Cristóbal',s='Santa Cruz'))\n",
        "print(\"Salto de línea \\n\")\n",
        "print(\"Arriba hay un salto de línea\")"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hola  IN6531\n",
            "Mi nombre es Cristóbal y soy de Santa Cruz\n",
            "Salto de línea \n",
            "\n",
            "Arriba hay un salto de línea\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-el0rDarsSh"
      },
      "source": [
        "####1.3 Listas\n",
        "\n",
        "Las listas son cojuntos de elementos ordenados y **mutables**\n",
        "\n",
        "[Link de utilidad para listas](https://uniwebsidad.com/libros/python/capitulo-7/metodos-de-agregado)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rIEsZ4yqvLwC",
        "outputId": "72988038-79ba-4208-febb-dcf181d58623"
      },
      "source": [
        "lista = [2,3,5,7,11]\n",
        "lista"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 3, 5, 7, 11]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5z1cPQuw1df"
      },
      "source": [
        "<img src=\"https://github.com/jakevdp/WhirlwindTourOfPython/raw/f40b435dea823ad5f094d48d158cc8b8f282e9d5/fig/list-indexing.png\"  width=\"500\" height=\"500\">"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLYVvaiIvLyq",
        "outputId": "2b02805b-09fb-4222-8c11-99ec9ec7db3c"
      },
      "source": [
        "#Primer elemento\n",
        "lista[0]"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0c8o6CNw7fe",
        "outputId": "40e876e9-1124-4220-8f5a-813a85429f7b"
      },
      "source": [
        "#Ultimo elemento\n",
        "lista[-1]"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r8zvXw4vw7mJ",
        "outputId": "92efe539-9bdc-4e8e-8011-13df7c9049c0"
      },
      "source": [
        "#Los dos primeros elementos\n",
        "lista[0:2]"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 3]"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTTFdSIpw7n4",
        "outputId": "bd8a83a2-a74a-489d-f8a0-a5078242a992"
      },
      "source": [
        "#Ivertir orden de lista\n",
        "lista[::-1]"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[11, 7, 5, 3, 2]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hu-zj6Bew7qi",
        "outputId": "2f1bff11-ac7c-411e-af64-0a0cab562815"
      },
      "source": [
        "lista"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 3, 5, 7, 11]"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBR5tz8Tyvq5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "557c8a24-7c1c-491b-aab7-a5c8534cac8c"
      },
      "source": [
        "l2= lista[::-1]\n",
        "print(lista)\n",
        "print(l2)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2, 3, 5, 7, 11]\n",
            "[11, 7, 5, 3, 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1_mdt8uw7iN",
        "outputId": "2a0821d4-8db7-4cd4-93d9-19cb6afe623b"
      },
      "source": [
        "#Podemos almacenar lo que queramos\n",
        "listaFalsa = [2,5,7,'Hola','IN6531',[34,65,21]]\n",
        "listaFalsa"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 5, 7, 'Hola', 'IN6531', [34, 65, 21]]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KAaIKCs5w7uB",
        "outputId": "d3279d57-7313-4ad9-940a-ae753733e95f"
      },
      "source": [
        "#Actualizar un elemento\n",
        "listaFalsa[0] = 6\n",
        "listaFalsa"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6, 5, 7, 'Hola', 'IN6531', [34, 65, 21]]"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ycKQ8V23w7vh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5a0c164-554c-4cc5-a9dd-4764cf96fb29"
      },
      "source": [
        "#Incluso si son de distinto tipo\n",
        "listaFalsa[-1] = 8\n",
        "listaFalsa"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6, 5, 7, 'Hola', 'IN6531', 8]"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1aoCA-YBw7yM",
        "outputId": "7681101f-d95e-4f31-a25d-aa2815858891"
      },
      "source": [
        "#largo de lista\n",
        "len(lista)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d64MtmkyzMLV",
        "outputId": "a62c272b-724c-436a-f503-074369b67588"
      },
      "source": [
        "#agregar elementos a una lista\n",
        "print(lista)\n",
        "lista.append(55)\n",
        "print(lista)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2, 3, 5, 7, 11]\n",
            "[2, 3, 5, 7, 11, 55]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBD17L85zheq",
        "outputId": "c7112bc0-3fd8-4e38-e9b3-158dc7e9184b"
      },
      "source": [
        "#eliminar elemento de una lista\n",
        "lista.pop(0) #Elimina primer elemento\n",
        "lista"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 5, 7, 11, 55]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zcl9lIG1YCG",
        "outputId": "2e64902d-e0b5-4754-a41a-53fbca42c430"
      },
      "source": [
        "#¿en que posicion esta un elemento especifico?\n",
        "lista.index(11)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNjSx5Z_zKYv"
      },
      "source": [
        "Existen mas metodos para incorporar/eliminar elementos, para mas información ver el link sugerido arriba."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pz65kbFRrsVW"
      },
      "source": [
        "####1.4 Tuplas\n",
        "\n",
        "Las tuplas son conjuntos de elementos ordenados e inmutables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMwdmTdC0wkc",
        "outputId": "89f0d556-5b82-4149-dea6-ff7e1c4accb0"
      },
      "source": [
        "tupla = (1,'hi')\n",
        "tupla"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 'hi')"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UA3uuTO80wnd",
        "outputId": "adc813ae-0b79-4bdb-aa42-00439420f11e"
      },
      "source": [
        "#indexa igual que una lista\n",
        "tupla[0]"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1npuxo2K0wry",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "620090c9-f63e-41aa-d114-4e46ce6cc46f"
      },
      "source": [
        "#NO SE PUEDE MUTAR\n",
        "tupla[1] = 'bye'"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-74-a9dea0151dc3>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#NO SE PUEDE MUTAR\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtupla\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'bye'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nd7bcHr80wt0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "2f67df7e-bdf3-4797-d25f-0dd0b7e7b73a"
      },
      "source": [
        "#NO SE PUEDEN AGREGAR ELEMENTOS\n",
        "tupla.append(3)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-75-49d0bb395490>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#NO SE PUEDEN AGREGAR ELEMENTOS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtupla\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3s-v3GCu0wCO"
      },
      "source": [
        "####1.5 Diccionarios\n",
        "\n",
        "Un diccionario es un conjunto no ordenado de elementos, a cada elemento se puede acceder a través de una llave."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12U15HO10wyA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0eb9f6c6-1888-4ff1-9af8-831ffdba008e"
      },
      "source": [
        "mydict = {\"elem1\": 1,\n",
        "          \"elem2\": \"Hello\",\n",
        "          \"elem3\": [1, 2, 3]}\n",
        "mydict"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'elem1': 1, 'elem2': 'Hello', 'elem3': [1, 2, 3]}"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-OF2EgyI0n2"
      },
      "source": [
        "dicc = {'Serie1': [1,2,3,4,5],\n",
        "        'Serie2':[5,6,8,9,10]}"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6eIha9O0wzn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d90c70b0-b0f5-4e69-bf85-e59bd4c5b44b"
      },
      "source": [
        "# acceder a un valor a través de su llave\n",
        "mydict[\"elem3\"]"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3]"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBY45oAv0w1Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "fc7c4c69-f847-4c7d-814a-66722bc1262c"
      },
      "source": [
        "# ERROR: la indexación es lo por la llave, no por posición ya que es un conjunto no ordenado\n",
        "mydict[0]"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-79-297e087d4e59>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# ERROR: la indexación es lo por la llave, no por posición ya que es un conjunto no ordenado\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmydict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m: 0"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K7nMswjNqkb_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2412f1d4-b4f8-4248-8697-83a6a6d2293c"
      },
      "source": [
        "mydict.keys()\n",
        "#HINT/SPOILER: si se puede iterar sobre esto"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['elem1', 'elem2', 'elem3'])"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzHjzSES2_k8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "233ad24d-e380-4c86-9852-40b06be80fcc"
      },
      "source": [
        "#Obtener las llaves como una lista\n",
        "keys = list(mydict.keys())\n",
        "keys"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['elem1', 'elem2', 'elem3']"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksyxzCwc26S1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50645a7a-181a-42d2-d898-7155e2db1143"
      },
      "source": [
        "# agregar un elemento\n",
        "mydict[\"elem4\"] = (1,2,3)\n",
        "mydict"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'elem1': 1, 'elem2': 'Hello', 'elem3': [1, 2, 3], 'elem4': (1, 2, 3)}"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8mwUdNnK26Vs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a6a052c-8698-447d-a7fc-96329c621559"
      },
      "source": [
        "# obtener una lista con todos los valores del diccionario\n",
        "values = list(mydict.values())\n",
        "values"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 'Hello', [1, 2, 3], (1, 2, 3)]"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j5TOl6iY3MUf"
      },
      "source": [
        "####1.6 Booleanos\n",
        "\n",
        "Valores posibles de un objeto booleano: True, False, None <br>\n",
        "\n",
        "\n",
        "Operadores de comparación:\n",
        "\n",
        "| Operación | Descripción |\n",
        "|--------|----------------|\n",
        "|``a == b``|``a`` igual a ``b``|\n",
        "|``a != b``| ``a`` diferente de ``b``|\n",
        "|``a < b``| ``a`` menor que ``b``|\n",
        "| ``a > b``| ``a`` mayor que ``b``             |\n",
        "|``a <= b``|``a`` menor o igual a ``b``|\n",
        "| ``a >= b``| ``a`` mayor o igual a ``b`` |\n",
        "|``a and b``|``a`` y ``b``|\n",
        "|``a & b``|``a`` y ``b``|\n",
        "| ``a or b`` | ``a`` o ``b`` |\n",
        "| ``a \\| b`` | ``a`` o ``b`` |\n",
        "| ``a ^ b`` | solo ``a`` o solo  ``b`` |"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HqsVOMHj26Zo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c10f49b4-144d-4f82-90f9-6d90815e0fd7"
      },
      "source": [
        "#operador or\n",
        "True | False"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jFVl4fD26bm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5631ec4-24ef-41f1-de12-e58a5f536b4d"
      },
      "source": [
        "True or False"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ic2AJUNt26gN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91abc4bf-2101-48dc-907a-2f277d2915c3"
      },
      "source": [
        "#operador xor\n",
        "True ^ True"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbn9JZFf26i5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b94d1678-58b0-4d35-db93-c37d1635b1d1"
      },
      "source": [
        "False ^ True"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dr4KJJxo3lXA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae443726-ae67-4506-a1df-a23f6308eec7"
      },
      "source": [
        "# operador and\n",
        "True & True"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pto1ziSL3laL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "293b8896-bbd8-4c94-b3ac-ad3a77bcf2d5"
      },
      "source": [
        "# 1 es igual a 0?\n",
        "1==0"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOGJSA-R3ld6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da91c12b-b790-42e4-c09c-7deb90be1eff"
      },
      "source": [
        "# 1 es distinto de 0?\n",
        "1!=0"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XaCedcJY3lfs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8013a0a-6901-42cd-b3ce-e8ce930fb1d9"
      },
      "source": [
        "# 3 es mayor a 2 y 1 es menor a 2?\n",
        "(3>2) and (1<2)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "ZZHhjzag3ljY",
        "outputId": "bb9f336a-4783-4445-f7be-4eb8405c2e7e"
      },
      "source": [
        "# Puedo usar esto sobre strings\n",
        "x > m"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-92-6b4b8af1e1bc>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Puedo usar esto sobre strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'x' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHD4uX6526mx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b59d72f4-b8c3-4f28-a624-82b6512b5d04"
      },
      "source": [
        "'x' > 'm'"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mumo3nnJ38yw",
        "outputId": "0aa0a16e-b785-4ea1-cbe7-7e7ff23ade95"
      },
      "source": [
        "#¿Y para distinto tamaño?\n",
        "'hola' > 'hola mundo'"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e_kXW7454Tap"
      },
      "source": [
        "##2.Control del flujo, Iteradores y Funciones"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nY3pO9kF4Zb7"
      },
      "source": [
        "####2.1 Control de flujo\n",
        "Una estructura de control, es un bloque de código que permite agrupar instrucciones de manera controlada.\n",
        "Las estructuras de control de flujo condicionales, se definen mediante el uso de tres palabras claves reservadas del lenguaje: `if` (si), `elif` (sino, si) y `else` (sino)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjIFfDzi4n1r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1a88415-4258-4f39-8b3e-127c4306143e"
      },
      "source": [
        "a = 3\n",
        "b = 2\n",
        "#\n",
        "if a < b:\n",
        "    print('se cumplió la condición 1')\n",
        "elif a == b:\n",
        "    print('no se cumplió la condición 1, pero si la 2')\n",
        "else:\n",
        "    print('no se cumplió ninguna de las condición anteriores')"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "no se cumplió ninguna de las condición anteriores\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VSqPa0vn4tKw"
      },
      "source": [
        "####2.2 Iteradores\n",
        "\n",
        "Los iteradores nos permiten dar una instruccion o generar una accion sobre varios elementos (si, les di basicamente la definicion de iterar)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xH2s1I4k5Nor"
      },
      "source": [
        "#####Range\n",
        "`Range(start, stop, step)` devuelve un objeto que produce una secuencia de enteros desde el inicio (inclusive) detener (exclusivo) por pasos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9g-jo0GN5LOs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "346fa671-63c9-4799-8701-0bcf3368613c"
      },
      "source": [
        "list(range(10))"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ajIbxiDs5LRL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4673531f-ba28-4541-aa99-740ea7b32fa8"
      },
      "source": [
        "list(range(1, 10))"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZKl5LZw5LUD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20919c7c-6c7a-4677-b728-220e85b66329"
      },
      "source": [
        "list(range(1, 12, 3))"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 4, 7, 10]"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhrKMREb5Un0"
      },
      "source": [
        "#####For\n",
        "La instrucción `for` itera con una variable que toma valores de una lista dada. A menudo, esa lista se especifica mediante `range`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1zYEHlF5LXY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0294ada-ab7d-490a-a1ea-05f08e432117"
      },
      "source": [
        "#se puede iterar sobre los valores de una lista\n",
        "lista = [1,2,3,4,5,6,7,8]\n",
        "for var in lista:\n",
        "  numero = var **2\n",
        "  print(numero)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "4\n",
            "9\n",
            "16\n",
            "25\n",
            "36\n",
            "49\n",
            "64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTMUGVd-PiBb",
        "outputId": "5c9900cb-727f-4c44-d345-053be1895a54"
      },
      "source": [
        "list(range(len(lista)))"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 2, 3, 4, 5, 6, 7]"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9RzPd9T5Ley",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58fe6084-e084-4174-d2c4-30f69b489c53"
      },
      "source": [
        "#se puede iterar sobre los indices de una lista\n",
        "for i in range(len(lista)):\n",
        "  print(i)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "biwXL_uf5Lkp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c58a6c80-2bc4-47c8-c86d-ab662b5624af"
      },
      "source": [
        "#Se puede iterar sobre una lista\n",
        "lista2 = []\n",
        "\n",
        "for i in lista:\n",
        "  numero2 = i**3\n",
        "  lista2.append(numero2)\n",
        "print(lista2)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 8, 27, 64, 125, 216, 343, 512]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMmoPeoT5Lh1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba9c51a8-c330-4296-d965-15604271f50b"
      },
      "source": [
        "#se pueden incorporar controles de flujo\n",
        "lista2 = []\n",
        "print(lista2)\n",
        "for i in lista:\n",
        "  numero = i**2\n",
        "  if numero%2 == 0:\n",
        "      lista2.append(numero)\n",
        "print(lista2)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[]\n",
            "[4, 16, 36, 64]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63NOSUqf5Lnb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52937384-640e-4539-88db-fdd638c10dd0"
      },
      "source": [
        "#List comprehension:\n",
        "#Es una forma de construir una nueva lista aplicando una expresión a cada elemento de otra lista (o a elementos de una secuencia)\n",
        "# y filtrando elementos según una condición, todo en una sola línea de código.\n",
        "l2 = [i**2 for i in lista]\n",
        "print(l2)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 4, 9, 16, 25, 36, 49, 64]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gQedlZ8ROzn",
        "outputId": "38149662-143a-485d-a207-8749c2f549a2"
      },
      "source": [
        "li = [i-1 for i in l2]\n",
        "print(li)"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 3, 8, 15, 24, 35, 48, 63]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BbueZeyf5Lp3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "055847fa-8947-4f3e-c65f-d669846e65ea"
      },
      "source": [
        "#mas ejemplos de list comprehension\n",
        "l2 = [i**2 for i in lista if i%2 == 0]\n",
        "print(l2)\n",
        "\n",
        "l2 = [1 if i%2==0 else 0 for i in lista]\n",
        "print(l2)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4, 16, 36, 64]\n",
            "[0, 1, 0, 1, 0, 1, 0, 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNDBdR9H7aiV"
      },
      "source": [
        "#####While\n",
        "\n",
        "La instrucción `While` itera con una variable entregada que va mutando *dentro*. Requiere partir de una condicion verdadera"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QuKvRztx7peV"
      },
      "source": [
        "#, es decir 'while (condition) ==False'\n",
        "i=20\n",
        "while (i<10):\n",
        "    print('iteración:', i)\n",
        "    i+=1"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bKTAh8j8HCd"
      },
      "source": [
        "Veamos un ejemplo \"util\":\n",
        "\n",
        "\n",
        "\n",
        "Si tenemos el polinomio:\n",
        "\n",
        "$$\n",
        "\\begin{align}\n",
        "P(x) &= 5+2x-3x^2+4x^3\n",
        "\\end{align}\n",
        "$$\n",
        "Se puede factorizar como:\n",
        "$$\n",
        "\\begin{align}\n",
        "P(x) &=5+x(2+x(-3+x(4)))\n",
        "\\end{align}\n",
        "$$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NrObmeD7_BN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "423ea8ba-81ce-4786-daba-f09461edad9b"
      },
      "source": [
        "# Definición de la lista de números y la variable 'x'\n",
        "a = [5, 2, -3, 4]\n",
        "x = 2\n",
        "# Inicialización de la variable de acumulación 'P' y su impresión\n",
        "P = 0\n",
        "print(P)\n",
        "# Inicialización de la variable de control del bucle 'i' con la longitud de la lista 'a'\n",
        "i = len(a)\n",
        "# Bucle 'while' que se ejecuta mientras 'i' sea mayor que 1\n",
        "while i > 1:\n",
        "    # Actualización de 'P' multiplicando el valor acumulado actual más el elemento en la posición 'i - 1' por 'x'\n",
        "    P = (P + a[i - 1]) * x\n",
        "    # Impresión del valor actual de 'P'\n",
        "    print(P)\n",
        "    # Decremento de 'i' para avanzar a la siguiente posición de la lista\n",
        "    i -= 1\n",
        "# Suma del primer elemento de la lista 'a' al valor final de 'P'\n",
        "P += a[0]\n",
        "# Impresión del valor final de 'P'\n",
        "print(P)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "8\n",
            "10\n",
            "24\n",
            "29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJ1pnaMo84m2"
      },
      "source": [
        "####2.3 Funciones\n",
        "\n",
        "Para definir una función se usa la siguiente sintaxis:\n",
        "\n",
        "\n",
        "```\n",
        "  def nombre_función(inputs):\n",
        "      declaraciones ....\n",
        "```\n",
        "Ejemplo:\n",
        "```\n",
        "  def myfun(x, y, z=default):\n",
        "      declaraciones ....\n",
        "```\n",
        "\n",
        "Llamar una función\n",
        "\n",
        "```\n",
        "  myfun(a, b)\n",
        "\n",
        "  myfun(x=a, y=b)\n",
        "\n",
        "  myfun(x=a, y=b, z=c)\n",
        "\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MoRFIY6_7p7N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b12e79f6-6a40-4c57-c69f-db2af09c5206"
      },
      "source": [
        "# una función puede no recibir parámetros\n",
        "def hello_world():\n",
        "    print(\"Hello World\")\n",
        "\n",
        "hello_world()"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello World\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U572KHyh7p9s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "062bfa61-9d43-4285-8b5e-52dc51c43c35"
      },
      "source": [
        "# función con argumento\n",
        "def greetings(name):\n",
        "    print('Hello', name)\n",
        "greetings('Joaquín')"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello Joaquín\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9qHR6GrT1nQ",
        "outputId": "99cd1696-59c9-426d-98eb-b3ce6df65737"
      },
      "source": [
        "def greetings(name='Joaquín'):\n",
        "    print('Hello', name)\n",
        "\n",
        "greetings(name='Cristóbal')"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello Cristóbal\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5YertYi89vpM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65b69046-fa15-4cb6-9695-bc58a2a755f3"
      },
      "source": [
        "# función con valor por defecto\n",
        "def mysum(x, y=5):\n",
        "    print(x+y)\n",
        "mysum(5)\n",
        "mysum(5, 10)"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n",
            "15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63TQ9DcK9vr0"
      },
      "source": [
        "# con el comando return una función puede retornar un objeto\n",
        "def is_positive(x):\n",
        "    if x>0:\n",
        "        return True\n",
        "    else:\n",
        "        return False\n",
        "a = is_positive(1)"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LOKcyj-yUPBf",
        "outputId": "73bba479-4024-4f3d-d7a4-d1c53fef1025"
      },
      "source": [
        "a"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDd2Rack9vu_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d176d16-bf31-40b7-a80d-607b07acb536"
      },
      "source": [
        "mylist = [1, 2, 3, 4]\n",
        "# list -> int\n",
        "# Esta funcion suma los valores de una lista dada\n",
        "def mysum(x):\n",
        "    s = 0\n",
        "    #esto es recurrente sobre blabla\n",
        "    for value in x:\n",
        "        s = s+value\n",
        "    return s\n",
        "\n",
        "mysum(mylist)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GanpIJ-O9v0L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a2c8527-5fbd-4bd8-8e38-1e7d91499f82"
      },
      "source": [
        "def promedio(x):\n",
        "    N = len(x)\n",
        "    return mysum(x)/N\n",
        "\n",
        "promedio(mylist)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.5"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4G9u2-M-V-d"
      },
      "source": [
        "El **scope** o alcance de una variable se refiere a los lugares en que se puede ver o acceder a una variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NjqiEBD99v5Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cecfc0a-5df8-4445-8e2d-2a02fc794a26"
      },
      "source": [
        "# las variables definidas dentro de una función no tienen impacto en las variables definidas a fuera\n",
        "x = 20\n",
        "def  myfun(y):\n",
        "    x = y\n",
        "    return x\n",
        "\n",
        "print(x)\n",
        "myfun(10)\n",
        "print(x)"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20\n",
            "20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X-_A6MC4-BMl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18fd8781-6da9-4e7b-d708-f147fb63cd14"
      },
      "source": [
        "#Notar que pasa si la asigno\n",
        "a=0\n",
        "print(a)\n",
        "a = myfun(10)\n",
        "print(a)\n",
        "print(x)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "10\n",
            "20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71xpBndMWEWV"
      },
      "source": [
        "t=10"
      ],
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vof16-9G-BPm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "037176ee-e302-491f-d472-26b64eb3fbef"
      },
      "source": [
        "def  my_funct(y):\n",
        "    return t+y\n",
        "\n",
        "my_funct(10)"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTP39-eN-XAF"
      },
      "source": [
        "Con **global** las declaraciones dentro de la función al ejecutarse pueden cambiar al valor original de la variable definida a fuera de la función. En general no se recomiendo el uso de \"global\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdwM_ITA-BSe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "075085c8-1e19-4acd-fd67-53ce6559349f"
      },
      "source": [
        "x = 20\n",
        "def  my_funct(y):\n",
        "    global x\n",
        "    x = y\n",
        "    return x\n",
        "\n",
        "print(x)\n",
        "# actualizar x\n",
        "my_funct(10)\n",
        "print(x)"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20\n",
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eg3DqN5kqdji"
      },
      "source": [
        "##3.Programación orientada a objetos (OOP)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ntUgkIPM-12M"
      },
      "source": [
        "1. Clase: Propiedad y comportamiento de un objeto concreto.\n",
        "2. Objeto: Instancia de una clase\n",
        "3. Atributo: Propiedad del objeto\n",
        "4. Método: Lo que un objeto puede hacer\n",
        "\n",
        "\n",
        "\n",
        "Más documentación<br><br>\n",
        "Métodos especiales: https://docs.python.org/3/reference/datamodel.html#special-method-names<br>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmSSzEZJjMQw"
      },
      "source": [
        "class Book():\n",
        "\n",
        "    #Método constructor: Función para instanciar un objeto.\n",
        "    def __init__(self, title, author, pages):\n",
        "        print(\"A book is created\") #mensaje que se muestra en patalla al crear un objeto\n",
        "        #Atributos de cada objeto\n",
        "        self.title = title\n",
        "        self.author = author\n",
        "        self.pages = pages\n",
        "    #Métodos especiales\n",
        "    def __str__(self): #al utilizar la función print() sobre un objeto retorna un string con todos sus atributos de forma:\n",
        "        return \"Title:%s , author:%s, pages:%s \" %(self.title, self.author, self.pages)\n",
        "    def __len__(self):#al utilizar la función len() sobre un objeto retorna el número de páginas del libro\n",
        "        return self.pages\n",
        "    #método\n",
        "    def saludar(self, saludo):\n",
        "        return '{s} {autor}'.format(s=saludo, autor=self.author)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cxo1OpmC_Pkl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c8fba04-43b0-45e0-973f-59887434060c"
      },
      "source": [
        "# instanciar un objeto\n",
        "book = Book(\"Machine Learning A Probabilistic Perspective\", \"K. Murphy\", 1000)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A book is created\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sKc2wBso_PnN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21ad3bf3-875b-414e-a7b1-03fbddf0fa0c"
      },
      "source": [
        "book"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<__main__.Book at 0x7b2175bcb7c0>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAInOvfY_PqR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2c7d4441-fa1d-4f95-a4e0-5c61bd4cad2d"
      },
      "source": [
        "# acceder a un atributo de un objeto\n",
        "book.title"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Machine Learning A Probabilistic Perspective'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ALjzU0s_PtN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "439de8c2-8038-4181-fb26-90f0c1b0b645"
      },
      "source": [
        "# método especial: print\n",
        "print(book)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Title:Machine Learning A Probabilistic Perspective , author:K. Murphy, pages:1000 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfcXHWTI_PwD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4708673d-a534-47fc-8da9-a6f9b00a8338"
      },
      "source": [
        "# método especial:len\n",
        "len(book)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Amh6NCZk_Py5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ab369fd4-e7fb-4b97-fa0d-614d9de35532"
      },
      "source": [
        "# método normal\n",
        "book.saludar('Hola')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Hola K. Murphy'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wljUi-AL_sl2"
      },
      "source": [
        "A efectos de este curso entender la noción de cómo funcionan las clases es lo único que se les pedirá, de esa manera podrán entender como funcionan las librerías que veremos más adelante.\n",
        "\n",
        "Para profudizar el conocimiento los invito a seguir con los cursos del MDS tomando *MDS7202 - Laboratorio de Programación Cientifica*."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I0O8Y61VANK-"
      },
      "source": [
        "##4.Librerías más usadas del curso"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6_t_7GtmAuiF"
      },
      "source": [
        "Instalar librerías en python, comandos: `pip` y `conda`\n",
        "\n",
        "Por ejemplo:\n",
        "\n",
        "`!pip install numpy`\n",
        "\n",
        "o\n",
        "\n",
        "`conda install numpy`\n",
        "\n",
        "Más documentación:\n",
        "\n",
        "https://packaging.python.org/tutorials/installing-packages/\n",
        "\n",
        "https://conda.io/docs/user-guide/tasks/manage-pkgs.html#installing-packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z1QL7KWxArFt"
      },
      "source": [
        "###4.1 Numpy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5aUF9tvDHb3P"
      },
      "source": [
        "Librería para operaciones numéricas con arreglos(arrays).\n",
        "\n",
        "\n",
        "\n",
        "<a href=\"https://www.datacamp.com/community/tutorials/python-numpy-tutorial?utm_source=adwords_ppc&utm_campaignid=1455363063&utm_adgroupid=65083631748&utm_device=c&utm_keyword=&utm_matchtype=b&utm_network=g&utm_adpostion=&utm_creative=332602034358&utm_targetid=aud-763347114660:dsa-473406585115&utm_loc_interest_ms=&utm_loc_physical_ms=1003325&gclid=Cj0KCQjwwOz6BRCgARIsAKEG4FVnhnyGmiRbRxrI9GO4XNje8eYOIoSvYPnJO911uEoBRGYvFo7FEroaAuUwEALw_wcB\">Tutorial en datacamp</a>\n",
        "\n",
        "\n",
        "100 numpy exercises\n",
        "\n",
        "https://drive.google.com/file/d/1GeqpxhbqXDv1Fq8GshnbdP6oUvpxSQv9/view"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XAXrYh2u_P1V",
        "outputId": "276aa165-6a03-4878-ea0d-ddee90a4643d"
      },
      "source": [
        "!pip install numpy"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.23.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kabOBHtjMcu"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAH1HeGAH2t-",
        "outputId": "3f077c2c-4318-4f84-a3ae-c53992fb3119"
      },
      "source": [
        "#transformar lista en array\n",
        "l = [1,2,3]\n",
        "print(l)\n",
        "x = np.asarray(l)\n",
        "print(x)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 2, 3]\n",
            "[1 2 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#crear un array\n",
        "x = np.array([1,2,3])"
      ],
      "metadata": {
        "id": "zjWr3GFsPKZ-"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VtUn-NYrH2xF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b651b0a8-7b4e-4836-e14e-b1820663cf07"
      },
      "source": [
        "#largo de un array\n",
        "len(x)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wza_LJpH20R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21b35818-1f20-4818-97e6-f867d8cebe7e"
      },
      "source": [
        "# dimensión del vector\n",
        "x.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3,)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlaDT4V2H22-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2464d072-7b54-44d3-9c32-0182c0b9be5d"
      },
      "source": [
        "# vector fila (1 x N)\n",
        "y = np.array([[1, 2, 3]])\n",
        "y"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEqGZrlqH251",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af4c29f2-9c67-4a63-8d4e-d0a0c43164eb"
      },
      "source": [
        "y.shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dnc5f120H282",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e6b3a5d-779f-4839-c50d-d1e80bee0705"
      },
      "source": [
        "# vector columna (N x 1)\n",
        "# transponer y\n",
        "z = y.T\n",
        "z.shape"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vrsoLRpPH2_b",
        "outputId": "a6f28c87-5989-4f91-b26e-168a020357de"
      },
      "source": [
        "#matriz de dos filas y tres columnas\n",
        "matriz = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "matriz"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [4, 5, 6]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YGIBdIJGH3CE",
        "outputId": "ff7de2ce-62d9-4763-f8b4-bae80f15883d"
      },
      "source": [
        "# indexación (posicion_fila, posicion_columna)\n",
        "matriz[0,0]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PqFdQHL7Id1g",
        "outputId": "681e6dd5-40f1-4e33-a8b4-75e25cc6d545"
      },
      "source": [
        "matriz[0][0]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCWATK4uId4Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4fbb5c23-3607-4e0e-daed-e7ed355d3eba"
      },
      "source": [
        "# primera fila\n",
        "matriz[0]"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bzop0FFZId7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ed28bf4-ad0d-40c5-d254-328bde50d693"
      },
      "source": [
        "# primera columna\n",
        "\n",
        "matriz[:,0]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jy6DCPycId-j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2d6a313-b9c6-4f39-f16d-0dbefb0b2fe1"
      },
      "source": [
        "# largo de la matriz = número de filas\n",
        "len(matriz)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eskx0rBHIeBX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a95e89c0-da7f-496f-ce9b-b5f8e7cda80d"
      },
      "source": [
        "# dimensión de la matriz\n",
        "matriz.shape"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hEtP-RPIIeEY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e60e9b0-69d3-4ef8-abc5-6463931be1ae"
      },
      "source": [
        "# producto punto\n",
        "x = np.array([1,2,3])\n",
        "y = np.array([2,2,2])\n",
        "\n",
        "np.dot(x, y)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBHVNLpxIeKg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f0cd6d5-d2d4-433a-c623-c93ea858f4c1"
      },
      "source": [
        "matriz"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [4, 5, 6]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFPHOYMnIeNI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d30613b-5eaa-4ae1-eec9-b3ab48e83d4e"
      },
      "source": [
        "# producto matricial, la dimensión de las matrices debe ser: n1 x m y m x n2\n",
        "np.dot(matriz, matriz.T)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[14, 32],\n",
              "       [32, 77]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jD9QD8zcKMqH",
        "outputId": "a1a87668-cfde-46fe-abd5-ee4c81896273"
      },
      "source": [
        "#Se pueden aplicar condiciones sobre los arreglos\n",
        "array = np.arange(1,10)\n",
        "array"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CkwP-oB9KXde",
        "outputId": "2aed442a-1287-4690-9d68-820bf89f496c"
      },
      "source": [
        "condicion = array>4\n",
        "condicion"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False, False, False,  True,  True,  True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79rEc0lQKXgW",
        "outputId": "a2c4ca37-5c75-4108-e94e-2a7b23caed59"
      },
      "source": [
        "array[condicion]"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbMt6Z0UKXjJ",
        "outputId": "a22894a4-7d06-4be7-ed54-54fe3f4cece2"
      },
      "source": [
        "array[array>5]"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "az4QMkJGKXlB",
        "outputId": "754d6bc9-9228-4894-95bf-68139b4660fb"
      },
      "source": [
        "array[array>=5]=0\n",
        "array"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7T3HVFBK2Lh"
      },
      "source": [
        "#matrices de ceros\n",
        "ceros = np.zeros(3)\n",
        "ceros2d= np.zeros((3,4))"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fs-dsLxWK2N-",
        "outputId": "184d53c2-7e72-4e89-be77-ff28ea166d06"
      },
      "source": [
        "#matriz identidad\n",
        "np.eye(5)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0.],\n",
              "       [0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmVcDP8SMSnJ",
        "outputId": "21c458bc-156d-4989-a084-829f7319d74b"
      },
      "source": [
        "#vector denso:\n",
        "# np.linspace(lim inf, lim sup, cantidad de elementos)\n",
        "x = np.linspace(0, 10, 6)\n",
        "x"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  2.,  4.,  6.,  8., 10.])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTZSfuUzIePt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45bfeaec-2a16-46aa-f724-6d4177c28fb4"
      },
      "source": [
        "# logaritmo natural\n",
        "np.log(1)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-XXvXzaIv-n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f68a47c-80f6-4963-97b3-ba33df751315"
      },
      "source": [
        "# exponencial\n",
        "np.exp(1)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.718281828459045"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZIzI1TfPIwBo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81151857-1cb0-45ce-8f7d-d7962b371d6f"
      },
      "source": [
        "# número pi\n",
        "np.pi"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.141592653589793"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ErCLLcfIwEj",
        "outputId": "473c173d-4397-40d9-9be7-0c7d9b06c682"
      },
      "source": [
        "# promedio\n",
        "x=np.array([3,5,7,9,11])\n",
        "x.mean()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.0"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIUGbuzbIwG5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41a5c29c-01ac-4bd1-a95c-4a61fa370365"
      },
      "source": [
        "# desviación estándar\n",
        "x.std()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.8284271247461903"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKwo0uM6IwJc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af6488c3-a58a-4955-a042-e77d72ba70a2"
      },
      "source": [
        "# mínimo\n",
        "x.min()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TG9L89myLVRQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a880840-8e47-4f66-a927-bfa9280d76c8"
      },
      "source": [
        "# máximo\n",
        "x.max()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uhEaP7diLVT4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d5e07dc-b187-45f9-a265-186ac98ea1c8"
      },
      "source": [
        "print(\"mínimo de 'a': \", x.min())\n",
        "print(\"máximo de 'a': \", x.max())\n",
        "print(\"promedio de'a': \", x.mean())\n",
        "print(\"desviación de 'a': \", x.std())"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mínimo de 'a':  3\n",
            "máximo de 'a':  11\n",
            "promedio de'a':  7.0\n",
            "desviación de 'a':  2.8284271247461903\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tv3v8qwfLbgu"
      },
      "source": [
        "###4.2 Matplotlib\n",
        "\n",
        "Librería para graficar\n",
        "\n",
        "<a href=\"https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python?utm_source=adwords_ppc&utm_campaignid=1455363063&utm_adgroupid=65083631748&utm_device=c&utm_keyword=&utm_matchtype=b&utm_network=g&utm_adpostion=&utm_creative=332602034358&utm_targetid=aud-299261629574:dsa-473406587955&utm_loc_interest_ms=&utm_loc_physical_ms=1003325&gclid=Cj0KCQjwwOz6BRCgARIsAKEG4FWTkQ2gGIkA_VNbTYlSLzUdaMSaypIJ0R2rOy3Wal8nSHI6YTyuiMoaAkcUEALw_wcB\">Tutorial datacamp</a>\n",
        "\n",
        "\n",
        "\n",
        "Documentación\n",
        "\n",
        "https://matplotlib.org"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0LCuG79Lv5g"
      },
      "source": [
        "**Disclosure:** Puede que durante el curso veamos implementaciones con otras librerías como seaborn o pyplot, esta es la librería más usada y por eso la veremos en esta aux. De todas maneras si en sus entregas usan otra librería de graficos, es perfectamente válido."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UvGuY1OpLVWh"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "u741CH4OMM5y",
        "outputId": "b971234f-bd7c-400b-d913-8b9599c49595"
      },
      "source": [
        "#Grafico simple\n",
        "x = np.linspace(0, 10, 500)\n",
        "y = np.sin(x)\n",
        "\n",
        "# plot\n",
        "plt.plot(x,y, label=\"sin\")\n",
        "\n",
        "# leyenda\n",
        "plt.legend()\n",
        "\n",
        "# titulo y etiquetas\n",
        "plt.title(\"Título\")\n",
        "plt.xlabel(\"Eje x\")\n",
        "plt.ylabel(\"Eje y\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "bGWVMolfMM8x",
        "outputId": "da38e4ef-2b37-4fb9-f55e-77430c62c820"
      },
      "source": [
        "#Dos graficos en la misma figura\n",
        "y = x**2\n",
        "y2 = x**3\n",
        "\n",
        "plt.plot(x,y, 'r')\n",
        "\n",
        "plt.title('grafico')\n",
        "plt.xlabel('ejeX')\n",
        "plt.ylabel('ejeY')\n",
        "\n",
        "plt.plot(x,y2,'g')\n",
        "\n",
        "plt.legend(('rojo','verde'))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ET7d/CuQeau9ItIVIRERESkXeZTpui76NppWamh0nHxUiERERKXGOdJmOgqgQiYiISInLu0xHg7AGpl+moyAqRCIiIlKiLr5Mx7Mxz5p+mY6CqBCJiIhIicq7TEdkYCQPN3zY7DgFUiESERGREmMzbLz868sAPNPiGbzcvUxOVDAVIilxY8eOpXHjxmbHEBERE3y7+1t2nd5FsHcw/Zr3MzvO31IhEhERkRJhGAaTVk8C4KnmTxHkHWRyor+nQiTFJjs72+wIIiLiQFYcXsG64+vw8fBhYMuBZse5IhUiFzVz5kwiIyOx2Wz55t999908/vjjAHzzzTc0bdoUHx8fatSowbhx48jJybGva7FYmD59OnfddRf+/v689NJLAEyePJnw8HACAwPp06cPGRkZl73/f//7X+rWrYuPjw916tTh3XffLcFPKyIiZpi8ejIAjzd+nPCAcJPTXJku3VECDMMgLducy1z4efoV6nDG+++/n6effpqff/6Z9u3bA5CUlMSSJUv4/vvvWbVqFY888gjTpk3jpptuYv/+/fTt2xeAMWPG2F9n7NixTJ48mTfeeAMPDw8+++wzxo4dyzvvvEPbtm358MMPmTZtGjVq1LA/Z968eYwePZq3336bJk2asHnzZp544gn8/f3p3bt3MX8jIiJihs0nN7N0/1LcLe4Od5mOgqgQlYC07DQCJgWY8t4XRl7A38v/H9crV64cnTt3Zv78+fZC9MUXX1ChQgVuvfVWOnTowIgRI+wFpUaNGrz44osMGzYsXyF6+OGHeeyxx+yPe/ToQZ8+fejTpw8AEyZM4Mcff8y3lWjMmDG8+uqrdO/eHYDo6Gh27tzJe++9p0IkIuIkJv+au3XowQYPEl0u2uQ0/0y7zFxYXFwcX375JZmZmUDulpsePXrg5ubG1q1bGT9+PAEBAfbpiSee4OTJk/ku8tqsWbN8r7lr1y5atmyZb15MTIz9fmpqKvv376dPnz75XnvChAns37+/BD+tiIiUlr1n9vLFzi8AGNFmhMlpCkdbiEqAn6cfF0ZeMO29C6tr164YhsGiRYto3rw5q1at4vXXXwfgwoULjBs3zr4V52I+Pj72+/7+/7w16mIXLuR+L7NmzbqsOLm7uxfptURExDG98tsr2AwbXWp1oWF4Q7PjFIoKUQmwWCyF2m1lNh8fH7p37868efPYt28ftWvXpmnT3KsPN23alN27d3PdddcV6TXr1q3L2rVreeSRR+zzfv/9d/v98PBwIiMjOXDgAHFxccXzQURExGGcPH+S2VtnAzCibdnYOgQqRC4vLi6OO++8kx07dtCzZ0/7/NGjR3PnnXdStWpV7rvvPvtutO3btzNhwoS/fb2BAwfy6KOP0qxZM9q0acO8efPYsWNHvkHV48aN45lnniE4OJhOnTqRmZnJhg0bOHv2LEOGDCnRzysiIiXrjd/fIMuaRZuoNrSt2tbsOIWmMUQu7rbbbiM0NJTdu3fz8MN/XV+mY8eOLFy4kB9++IHmzZvTqlUrXn/9dapVq3bF13vwwQcZNWoUw4YN48Ybb+Tw4cP065f/zKT/+te/+O9//8sHH3xAw4YNadeuHbNnzyY62vEH3YmIyN87l3GO6RumA2Vr6xCAxTAMw+wQji4lJYXg4GCSk5MJCsp/ls2MjAwOHjxIdHR0vrE1Urz0PYuIOL6Jqyby/E/P0yCsAdv+vc30q9pf6ff7UtpCJCIiItcsPTudN35/A8g9sszsMlRUKkQiIiJyzT7Y8gGn0k5RPaQ6DzZ40Ow4RaZCJCIiItck25rNy7++DMBzMc/h4Vb2jtlSIRIREZFr8uG2DzmSfISIgAgeb/K42XGuigpRMdHY9JKl71dExDHl2HKYtHoSkLt1yNfT1+REV0eF6Bp5enoC5LuchRS/vO837/sWERHH8On2T9mXtI/yvuV5stmTZse5amVvJ5+DcXd3JyQkhMTERAD8/Ap3tXkpHMMwSEtLIzExkZCQEF3eQ0TEgdgMGy+tegmAITFDCPAy58LmxUGFqBhEREQA2EuRFL+QkBD79ywiIo7h611fs+v0LoK9g+nfvL/Zca6JClExsFgsVKpUibCwMLKzs82O43Q8PT21ZUhExMEYhsGEVbmXcnqm5TME+wSbnOjaqBAVI3d3d/1wi4iIS1i0dxFb4rcQ4BXAwJYDzY5zzTSoWkRERIrEMAwmrMzdOvRUs6co71fe5ETXToVIREREiuTHAz+y9vhafDx8GBIzxOw4xUKFSERERIokb+xQ36Z9CQ8INzlN8VAhEhERkUJbeXglKw+vxMvdi6Fthpodp9ioEImIiEih5Y0deqzxY1QJqmJymuKjQiQiIiKFsvbYWpYdWIa7xZ3hbYabHadYqRCJiIhIoeSNHep5Q0+iy0WbnKZ4qRCJiIjIP9p4YiML9yzEzeLGyLYjzY5T7FSIRERE5B+NXTEWgIcbPkztCrXNDVMCVIhERETkijac2GDfOjTq5lFmxykRKkQiIiJyRWN/GQtAXMM4ri9/vblhSogKkYiIiPyt9cfXs2jvIqfeOgQqRCIiInIFeWOHet7Qk1rla5kbpgSpEImIiEiB1h5by/d7v8fd4u7UW4dAhUhERET+xrgV44DcrUPXhV5ncpqSpUIkIiIil/n92O8s3rcYd4s7L9z8gtlxSpwKkYiIiFwmb+tQr0a9nH7rEKgQiYiIyCXWHF3Dkn1LcrcO3eT8W4dAhUhEREQukbd1qHej3tQMrWlymtKhQiQiIiJ2a46uYen+pXi4efD8zc+bHafUqBCJiIiIXd55h3o36k2NcjXMDVOKVIhEREQEgNVHVvPD/h9ytw7d5Dpbh0CFSERERADDMHj+p9wS9Hjjx4kuF21yotKlQiQiIiIsO7CMlYdX4u3uzah2zn1W6oI4dCGyWq2MGjWK6OhofH19qVmzJi+++CKGYdjXMQyD0aNHU6lSJXx9fYmNjWXv3r35XicpKYm4uDiCgoIICQmhT58+XLhwobQ/joiIiEO6eOtQv2b9qBJUxeREpc+hC9HLL7/M9OnTefvtt9m1axcvv/wyU6ZM4a233rKvM2XKFKZNm8aMGTNYu3Yt/v7+dOzYkYyMDPs6cXFx7Nixg2XLlrFw4UJWrlxJ3759zfhIIiIiDueb3d+w4cQG/D39GXnTSLPjmMJiXLy5xcHceeedhIeH8/7779vn3Xvvvfj6+vLRRx9hGAaRkZE8++yzPPfccwAkJycTHh7O7Nmz6dGjB7t27aJevXqsX7+eZs2aAbBkyRLuuOMOjh07RmRk5D/mSElJITg4mOTkZIKCgkrmw4qIiJjAarPS+L3GbE/czn/a/oeX2r9kdqRiU5Tfb4feQtS6dWuWL1/Onj17ANi6dSurV6+mc+fOABw8eJD4+HhiY2PtzwkODqZly5asWbMGgDVr1hASEmIvQwCxsbG4ubmxdu3aAt83MzOTlJSUfJOIiIgz+nTHp2xP3E6wdzDPtX7O7Dim8TA7wJWMGDGClJQU6tSpg7u7O1arlZdeeom4uDgA4uPjAQgPD8/3vPDwcPuy+Ph4wsLC8i338PAgNDTUvs6lJk2axLhx44r744iIiDiUbGs2Y34ZA8DQ1kMp51vO5ETmcegtRJ999hnz5s1j/vz5bNq0iTlz5vDKK68wZ86cEn3fkSNHkpycbJ+OHj1aou8nIiJihjlb57AvaR8V/SoysNVAs+OYyqG3EA0dOpQRI0bQo0cPABo2bMjhw4eZNGkSvXv3JiIiAoCEhAQqVapkf15CQgKNGzcGICIigsTExHyvm5OTQ1JSkv35l/L29sbb27sEPpGIiIhjyMzJZPyK8QCMbDuSAK8AkxOZy6G3EKWlpeHmlj+iu7s7NpsNgOjoaCIiIli+fLl9eUpKCmvXriUmJgaAmJgYzp07x8aNG+3r/PTTT9hsNlq2bFkKn0JERMTxvLfxPY6mHKVyYGX6Ne9ndhzTOfQWoq5du/LSSy9RtWpV6tevz+bNm3nttdd4/PHHAbBYLAwaNIgJEyZQq1YtoqOjGTVqFJGRkXTr1g2AunXr0qlTJ5544glmzJhBdnY2AwYMoEePHoU6wkxERMTZpGalMnHVRABG3TwKHw8fkxOZz6EL0VtvvcWoUaN46qmnSExMJDIykieffJLRo0fb1xk2bBipqan07duXc+fO0bZtW5YsWYKPz1//cOfNm8eAAQNo3749bm5u3HvvvUybNs2MjyQiImK6t9e9TUJqAjXK1eDxJo+bHcchOPR5iByFzkMkIiLOIjkjmeg3ozmbcZa53ebSq1EvsyOVGKc5D5GIiIgUr6m/TeVsxlnqVqjLww0fNjuOw1AhEhERcREnz5/k9d9fB2Bi+4m4u7mbnMhxqBCJiIi4iPErxpOWnUZMlRjurn232XEcigqRiIiIC9hzZg+zNs0C4OXYl7FYLCYnciwqRCIiIi7g+Z+ex2pYufP6O7mp2k1mx3E4KkQiIiJObt3xdXyx8wssWJjUfpLZcRySCpGIiIgTMwyD4T8OB6B34940CGtgciLHpEIkIiLixJbuX8ovh37B292bcbeMMzuOw1IhEhERcVI2w8aIH0cAMKDFAKoGVzU5keNSIRIREXFSH//xMVsTthLsHczItiPNjuPQVIhEREScUGZOJi/8/AIAw9sMp7xfeZMTOTYVIhERESc0Y8MMDp07RKWASgxsNdDsOA5PhUhERMTJJGck8+LKFwEYd8s4/Dz9TE7k+FSIREREnMzU36ZyJv0MtcvX5rEmj5kdp0xQIRIREXEix1KO8eqaVwGY1H4SHm4eJicqG1SIREREnMjzPz1PRk4GN1W9iW51upkdp8xQIRIREXESm05uYu7WuQC82uFVXcC1CFSIREREnIBhGDz7w7MAPNzwYZpXbm5yorJFhUhERMQJLNyz0H6Jjom3TTQ7TpmjQiQiIlLGZVuzGbpsKACDWg2iWkg1kxOVPSpEIiIiZdzMjTPZfWY3Ffwq6BIdV0mFSEREpAxLzkhm7IqxQO5JGIN9gs0NVEapEImIiJRhk1ZP4nTaaWqXr80TTZ8wO06ZpUIkIiJSRh06d4g3fn8DgKm3T8XT3dPcQGWYCpGIiEgZ9Z/l/yHTmsmt1W/lzuvvNDtOmaZCJCIiUgatO76Oj7d/jAWLTsJYDFSIREREyhjDMBiydAgAvRr1okmlJiYnKvtUiERERMqYT3d8yq9Hf8XP04+XbnvJ7DhOQYVIRESkDEnLTrOfhHFEmxFUCapiciLnoEIkIiJShkz5dQrHUo5RNbgqz7V+zuw4TkOFSEREpIw4knyEKb9OAeCV21/B19PX5ETOQ4VIRESkjBj+43DSc9K5udrN3FfvPrPjOBUVIhERkTJg1eFVfLL9EyxYeLPTmzrMvpipEImIiDg4q83KwCUDAXii6RM0jmhsbiAnpEIkIiLi4GZvmc3m+M0Eewcz4bYJZsdxSipEIiIiDiw5I5n//PQfAMa0G0NF/4omJ3JOKkQiIiIObMLKCSSmJlK7fG36t+hvdhynpUIkIiLioPae2cuba98E4PWOr+Pl7mVyIuelQiQiIuKghvwwhGxbNnfUuoPOtTqbHcepqRCJiIg4oEV7FrFwz0I83Dx4rcNrZsdxeipEIiIiDiYjJ4NnljwDwJBWQ6hdobbJiZyfCpGIiIiDmfLrFA6cPUDlwMqMajfK7DguQYVIRETEgRw8e5BJqycB8GqHVwnwCjA5kWtQIRIREXEgg5YOIiMng9uib+OB+g+YHcdlqBCJiIg4iEV7FvHt7m/xcPPg7c5v63plpUiFSERExAFcPJB6cKvB1K1Y1+RErkWFSERExAHkDaSODIxk1M0aSF3aVIhERERMdvFA6tc6vEagd6DJiVyPCpGIiIjJNJDafCpEIiIiJrp4IPVbnd/SQGqTqBCJiIiY5OKB1INaDqJexXomJ3JdKkQiIiImmbRqkn0g9eh2o82O49JUiEREREzw5+k/7QOp3+j4hgZSm0yFSEREpJQZhsG/F/6bbFs2d9S6g/vq3Wd2JJenQiQiIlLK5m6dy4rDK/D18OWdO97RQGoHoEIkIiJSik6nnebZH54FYOwtY6keUt3cQAKUgUJ0/PhxevbsSfny5fH19aVhw4Zs2LDBvtwwDEaPHk2lSpXw9fUlNjaWvXv35nuNpKQk4uLiCAoKIiQkhD59+nDhwoXS/igiIiIMWzaMM+lnaBjWkMGtBpsdR/6fQxeis2fP0qZNGzw9PVm8eDE7d+7k1VdfpVy5cvZ1pkyZwrRp05gxYwZr167F39+fjh07kpGRYV8nLi6OHTt2sGzZMhYuXMjKlSvp27evGR9JRERc2MrDK/lgywcAzLhzBp7uniYnkjwWwzAMs0P8nREjRvDrr7+yatWqApcbhkFkZCTPPvsszz33HADJycmEh4cze/ZsevTowa5du6hXrx7r16+nWbNmACxZsoQ77riDY8eOERkZ+Y85UlJSCA4OJjk5maCgoOL7gCIi4jIyczJp/F5j/jz9J0/e+CQz7pxhdiSnV5Tfb4feQvTtt9/SrFkz7r//fsLCwmjSpAmzZs2yLz948CDx8fHExsba5wUHB9OyZUvWrFkDwJo1awgJCbGXIYDY2Fjc3NxYu3Ztge+bmZlJSkpKvklERORaTP1tKn+e/pMw/zAmtZ9kdhy5hEMXogMHDjB9+nRq1arF0qVL6devH8888wxz5swBID4+HoDw8PB8zwsPD7cvi4+PJywsLN9yDw8PQkND7etcatKkSQQHB9unqKio4v5oIiLiQvYl7WPCyglA7jmHyvmW+4dnSGlz6EJks9lo2rQpEydOpEmTJvTt25cnnniCGTNKdjPjyJEjSU5Otk9Hjx4t0fcTERHnZRgGTy16ikxrJrfXuJ0eDXqYHUkK4NCFqFKlStSrl/+6LnXr1uXIkSMAREREAJCQkJBvnYSEBPuyiIgIEhMT8y3PyckhKSnJvs6lvL29CQoKyjeJiIhcjY+3f8yyA8vwdvfm3S7v6pxDDsqhC1GbNm3YvXt3vnl79uyhWrVqAERHRxMREcHy5cvty1NSUli7di0xMTEAxMTEcO7cOTZu3Ghf56effsJms9GyZctS+BQiIuKqTqWeYuCSgQCMunkU14VeZ3Ii+TseZge4ksGDB9O6dWsmTpzIAw88wLp165g5cyYzZ84EwGKxMGjQICZMmECtWrWIjo5m1KhRREZG0q1bNyB3i1KnTp3su9qys7MZMGAAPXr0KNQRZiIiIldr0NJBnE47zQ3hNzCszTCz48gVOHQhat68OV9//TUjR45k/PjxREdH88YbbxAXF2dfZ9iwYaSmptK3b1/OnTtH27ZtWbJkCT4+PvZ15s2bx4ABA2jfvj1ubm7ce++9TJs2zYyPJCIiLmLRnkXM/2M+bhY33r/rfZ1zyME59HmIHIXOQyQiIkWRkplC/XfrcyzlGM/FPMfUDlPNjuSSnOY8RCIiImXRiB9HcCzlGDXL1WTcrePMjiOFoEIkIiJSjFYdXsX0DdMBmNV1Fn6efiYnksJQIRIRESkmGTkZ/Ou7fwHwRNMnuDX6VpMTSWGpEImIiBST8SvGs+fMHioFVGLK7VPMjiNFoEIkIiJSDDaf3MyUX3NL0PQu0wnxCTE3kBSJCpGIiMg1yrHl0OfbPlgNK/fXu5+769xtdiQpIhUiERGRa/TKb6+wOX4z5XzK8Vbnt8yOI1dBhUhEROQa7EjcwZhfxgDwesfXCQ8INzmRXA0VIhERkauUbc2m94LeZFmz6FKrC480esTsSHKVVIhERESu0pRfp7Dx5EZCfEKY2XWmrmRfhqkQiYiIXIVtCdsYtyL3LNRvdX6LyEBdMLwsUyESEREporxdZdm2bO6ufTdxDeP++Uni0FSIREREimjiqolsid9CqG8oM+6coV1lTkCFSEREpAg2ndzEhFUTAHjnjneICIgwOZEUhyIVohMnTpRUDhEREYeXmZPJowseJceWw3317uPB+g+aHUmKSZEKUf369Zk/f35JZREREXFoL658kT8S/6CiX0XeveNd7SpzIkUqRC+99BJPPvkk999/P0lJSSWVSURExOGsP76eyasnA7nXKqvoX9HkRFKcilSInnrqKbZt28aZM2eoV68e3333XUnlEhERcRjp2en0XtAbq2GlR4Me3FvvXrMjSTHzKOoToqOj+emnn3j77bfp3r07devWxcMj/8ts2rSp2AKKiIiYbcSPI9h1ehfh/uG6VpmTKnIhAjh8+DBfffUV5cqV4+67776sEImIiDiLZfuXMW3dNAA+uPsDKvhVMDmRlIQiN5lZs2bx7LPPEhsby44dO6hYUftQRUTEOSWlJ/HoN48C8FSzp+hcq7O5gaTEFKkQderUiXXr1vH222/zyCO6gJ2IiDgvwzD498J/c+L8CWqXr83UDlPNjiQlqEiFyGq1sm3bNqpUqVJSeURERBzCR9s+4vOdn+Ph5sFH3T/Cz9PP7EhSgopUiJYtW5bv8b59+9i/fz8333wzvr6+GIahczKIiEiZd+jcIQYsHgDAmHZjaBbZzOREUtKu6tIdZ86cITY2luuvv5477riDkydPAtCnTx+effbZYg0oIiJSmqw2K498/QgpmSm0jmrNiLYjzI4kpeCqCtHgwYPx8PDgyJEj+Pn9tQnxwQcfZMmSJcUWTkREpLS98tsrrDqyigCvAD6850M83HQktSu4qn/KP/zwA0uXLr1sLFGtWrU4fPhwsQQTEREpbZtPbmbUz6MAeLPTm9QoV8PkRFJarmoLUWpqar4tQ3mSkpLw9va+5lAiIiKlLT07nZ5f9yTblk23Ot14rPFjZkeSUnRVheimm25i7ty59scWiwWbzcaUKVO49dZbiy2ciIhIaXn2h2fZeWon4f7hzLxzpg4ScjFXtctsypQptG/fng0bNpCVlcWwYcPYsWMHSUlJ/Prrr8WdUUREpER9vetrpm+YDsDce+bqwq0u6Kq2EDVo0IA9e/bQtm1b7r77blJTU+nevTubN2+mZs2axZ1RRESkxBxJPkKfb/sAMKz1MDrU7GByIjGDxTAMw+wQji4lJYXg4GCSk5MJCgoyO46IiBSTHFsOt825jVVHVtE8sjmrH1+Nl7uX2bGkmBTl97vQu8y2bdtGgwYNcHNzY9u2bVdcNyAggKioKDw9PQv78iIiIqVuwsoJrDqyikCvQD6+92OVIRdW6ELUuHFj4uPjCQsLo3HjxlgsFq60cSk4OJgZM2bw4IMPFktQERGR4rTy8EpeXPkiADPunEHNUA35cGWFLkQHDx60X9n+4MGDV1w3MzOTzz//nOHDh6sQiYiIwzmTdoa4r+KwGTZ6N+rNww0fNjuSmKzQg6qrVatmPwSxWrVqHDlyhOeff56HHnoIDw8PqlWrxsqVKzl69CjXX389Tz31FE2bNi2x4CIiIlfDMAz+9d2/OJZyjFqhtXj7jrfNjiQO4KqOMvvyyy/p2LEjvr6+bNq0iczMTACSk5OZOHEiAOXKleOrr74qvqQiIiLFYPqG6Sz4cwGebp58ct8nBHgFmB1JHMBVFaIJEyYwY8YMZs2alW/gdJs2bdi0aVOxhRMRESlO2xK2MWTpEABejn2ZppW0J0NyXVUh2r17NzfffPNl84ODgzl37ty1ZhIRESl2KZkp3PfZfWRaM7mj1h0MajXI7EjiQK6qEEVERLBv377L5q9evZoaNXQhPBERcSyGYdD3u77sTdpLlaAqzOk2R5fmkHyuqhA98cQTDBw4kLVr12KxWDhx4gTz5s3jueeeo1+/fsWdUURE5Jq8u/5dPt3xKR5uHnx232dU8KtgdiRxMFd1LbMRI0Zgs9lo3749aWlp3HzzzXh7e/Pcc8/x9NNPF3dGERGRq7b++HoGLx0MwJTYKcRExZicSBzRNV26Iysri3379nHhwgXq1atHQIBzjtTXpTtERMqms+lnaTqzKYfOHaJbnW589cBX2lXmQkrk0h0F8fLyol69etfyEiIiIiXCMAx6L+jNoXOHqFGuBh/c/YHKkPytqxpDJCIi4uhe+e0VvtvzHV7uXnx+/+eE+ISYHUkcmAqRiIg4ndVHVjNy+UgA3uz0ps43JP9IhUhERJxKYmoiD37xIFbDysMNH+bJG580O5KUASpEIiLiNHJsOTz05UOcOH+COhXq8N6d72nckBSKCpGIiDiN/yz/Dz8d/Al/T3++uP8LXadMCk2FSEREnMJnOz5j6m9TAfjg7g+oH1bf5ERSlqgQiYhImbc9cTuPf/M4AENbD+X++vebnEjKGhUiEREp085lnOOeT+8hNTuV9tHtmdh+otmRpAxSIRIRkTLLZtjo9XUv9iXto1pwNT657xM83K7pnMPiolSIRESkzBq/YjwL9yzEx8OHrx78ShdtlaumQiQiImXSwj0LGbdiHAAzuszQyRflmqgQiYhImbP3zF56ftUTgP7N+9O7cW+TE0lZV6YK0eTJk7FYLAwaNMg+LyMjg/79+1O+fHkCAgK49957SUhIyPe8I0eO0KVLF/z8/AgLC2Po0KHk5OSUcnoRESkOKZkpdPu0G8mZybSJasNrHV8zO5I4gTJTiNavX897773HDTfckG/+4MGD+e677/j8889ZsWIFJ06coHv37vblVquVLl26kJWVxW+//cacOXOYPXs2o0ePLu2PICIi18hqs/Lwlw+z89ROIgMj+fz+z/Fy9zI7ljiBMlGILly4QFxcHLNmzaJcuXL2+cnJybz//vu89tpr3Hbbbdx444188MEH/Pbbb/z+++8A/PDDD+zcuZOPPvqIxo0b07lzZ1588UXeeecdsrKyzPpIIiJyFUYuH8mivYvw8fDhmx7fUCmwktmRxEmUiULUv39/unTpQmxsbL75GzduJDs7O9/8OnXqULVqVdasWQPAmjVraNiwIeHh4fZ1OnbsSEpKCjt27CidDyAiItdszpY59jNRz757Ns0im5mcSJyJw5+s4ZNPPmHTpk2sX7/+smXx8fF4eXkREhKSb354eDjx8fH2dS4uQ3nL85YVJDMzk8zMTPvjlJSUa/kIIiJyjdYcXUPfhX0BeOGmF3iwwYMmJxJn49BbiI4ePcrAgQOZN28ePj4+pfa+kyZNIjg42D5FRUWV2nuLiEh+R5KP0O3TbmRZs7inzj2Mu3Wc2ZHECTl0Idq4cSOJiYk0bdoUDw8PPDw8WLFiBdOmTcPDw4Pw8HCysrI4d+5cvuclJCQQEREBQERExGVHneU9zlvnUiNHjiQ5Odk+HT16tPg/nIiI/KPUrFTu/uRuElMTuSH8BubeMxc3i0P/dEkZ5dD/VrVv354//viDLVu22KdmzZoRFxdnv+/p6cny5cvtz9m9ezdHjhwhJiYGgJiYGP744w8SExPt6yxbtoygoCDq1atX4Pt6e3sTFBSUbxIRkdJlM2z0XtCbLfFbqOhXkW97fEuAV4DZscRJOfQYosDAQBo0aJBvnr+/P+XLl7fP79OnD0OGDCE0NJSgoCCefvppYmJiaNWqFQAdOnSgXr169OrViylTphAfH88LL7xA//798fb2LvXPJCIihTN+xXi+3PUlnm6efP3g11QLqWZ2JHFiDl2ICuP111/Hzc2Ne++9l8zMTDp27Mi7775rX+7u7s7ChQvp168fMTEx+Pv707t3b8aPH29iahERuZJ52+bZL8vx3p3v0aZqG5MTibOzGIZhmB3C0aWkpBAcHExycrJ2n4mIlLAVh1bQ4aMOZFmzeC7mOaZ2mGp2JCmjivL77dBjiERExLXsPr2bez69hyxrFvfWvZeXb3/Z7EjiIlSIRETEIZxKPcUd8+/gbMZZWlVpxYf3fKgjyqTU6N80ERExXXp2Ond9chcHzh4gOiSab3p8g6+nr9mxxIWoEImIiKlsho1HFjzC78d+p5xPOb6P+54w/zCzY4mLUSESERFTjfhxBF/s/MJ+eH2dCnXMjiQuSIVIRERM896G9+wXbP3f3f+jXfV2JicSV6VCJCIipli0ZxH9v+8PwLhbxtHzhp4mJxJXpkIkIiKlbs3RNdz/+f1YDSu9G/Vm1M2jzI4kLk6FSEREStWuU7u48+M7Sc9Jp/N1nZnVdRYWi8XsWOLiVIhERKTUHEs5RsePOpKUnkTLyi35/P7P8XT3NDuWiAqRiIiUjrPpZ+n0USeOphyldvnaLHx4If5e/mbHEgFUiEREpBTknXhxx6kdRAZGsrTnUir4VTA7loidCpGIiJSoHFsOPb7sweojqwn2DmZJ3BKqhVQzO5ZIPipEIiJSYgzDoN/Cfny7+1u83b359qFvaRje0OxYIpdRIRIRkRLzwk8v8N/N/8XN4sbH937MzdVuNjuSSIFUiEREpERMXj2ZiasnAvDuHe9yT917TE4k8vdUiEREpNi9s+4dRi4fCcCU2Ck82exJkxOJXJkKkYiIFKs5W+YwYPEAAF646QWGthlqciKRf6ZCJCIixebLnV/y+LePA/BMi2cYf+t4kxOJFI4KkYiIFIsl+5bw0JcPYTNsPN74cV7v9LouySFlhgqRiIhcs5WHV9L90+5k27J5oP4DzOw6EzeLfmKk7NC/rSIick02nNjAnfNzL9bapVYXPrznQ9zd3M2OJVIkKkQiInLVNp3cRIcPO3A+6zy3Vr+Vz+//HC93L7NjiRSZCpGIiFyVzSc3Ezs3lrMZZ4mpEsM3Pb7B19PX7FgiV0WFSEREimxr/FZiP8wtQ62qtGJJzyUEegeaHUvkqqkQiYhIkWxL2Eb7ue1JSk+iZeWWLIlbQpB3kNmxRK6JCpGIiBTaHwl/0H5ue86kn6FF5RYs7bmUYJ9gs2OJXDMVIhERKZTtidu5be5tnE47TbPIZipD4lRUiERE5B/tSNzBbXNyy9CNlW7kh54/EOITYnYskWKjQiQiIle0LWEbt865lVNpp2haqSnLei2jnG85s2OJFCsVIhER+Vvrj6/nltm3qAyJ01MhEhGRAq0+spr2c9vbD61f/shyQn1DzY4lUiJUiERE5DLLDyyn40cdOZ91nluq36IxQ+L0VIhERCSfRXsW0WV+F9Ky0+hYsyOLHl6kky6K01MhEhERuy93fsk9n95DpjWTu2vfzTc9vsHP08/sWCIlToVIREQAmLdtHg9+8SDZtmx6NOjB5/d/jreHt9mxREqFCpGIiPDOunfo9XUvrIaVxxo/xkf3fISnu6fZsURKjQqRiIgLMwyDMT+PYcDiARgY9G/en//e9V/c3dzNjiZSqjzMDiAiIuaw2qw8vfhppm+YDsDYdmMZ3W40FovF5GQipU+FSETEBWXmZNLr6158vvNzLFh454536Ne8n9mxREyjQiQi4mLOZ57nnk/vYfnB5Xi6eTKv+zzur3+/2bFETKVCJCLiQhJTE7lj3h1sPLmRAK8Avn7wa2JrxJodS8R0KkQiIi7i4NmDdPyoI3uT9lLBrwKL4xbTLLKZ2bFEHIIKkYiIC1h3fB1dP+5KYmoi1YKr8UOvH7i+/PVmxxJxGDrsXkTEyS34cwG3zL6FxNREGkc05tfHf1UZErmECpGIiBN78/c36f5pd9Jz0ul8XWdWPrqSykGVzY4l4nBUiEREnJDVZmXg4oEMWjoIA4Mnb3ySbx/6VhdpFfkbGkMkIuJk0rLTiPsqjgV/LgDg5diXGdp6qE64KHIFKkQiIk4k4UICXT/uyvoT6/F292buPXN5oP4DZscScXgqRCIiTmJL/Bbu+vgujqYcJdQ3lG96fEPbqm3NjiVSJqgQiYg4gS93fskjCx4hLTuNWqG1WPjwQh1JJlIEGlQtIlKGGYbB+BXjue/z+0jLTuP2Grez9l9rVYZEikhbiEREyqjUrFQe++YxPt/5OQCDWg5iaoepeLjpT7tIUem/GhGRMuhI8hG6fdKNzfGb8XTzZHqX6fRp2sfsWCJllgqRiEgZ89vR37jn03tITE2kol9FvnrwKw2eFrlGKkQiImWEYRhM3zCdQUsGkW3L5obwG/i2x7dUC6lmdjSRMs+hB1VPmjSJ5s2bExgYSFhYGN26dWP37t351snIyKB///6UL1+egIAA7r33XhISEvKtc+TIEbp06YKfnx9hYWEMHTqUnJyc0vwoIiLXJC07jd4LetP/+/5k27K5r959/Pr4rypDIsXEoQvRihUr6N+/P7///jvLli0jOzubDh06kJqaal9n8ODBfPfdd3z++eesWLGCEydO0L17d/tyq9VKly5dyMrK4rfffmPOnDnMnj2b0aNHm/GRRESKbH/SfmLej+HDbR/ibnHnldtf4bP7PiPAK8DsaCJOw2IYhmF2iMI6deoUYWFhrFixgptvvpnk5GQqVqzI/Pnzue+++wD4888/qVu3LmvWrKFVq1YsXryYO++8kxMnThAeHg7AjBkzGD58OKdOncLLy+sf3zclJYXg4GCSk5MJCgoq0c8oInKxhXsW0vOrniRnJhPmH8an933KLdVvMTuWSJlQlN9vh95CdKnk5GQAQkNDAdi4cSPZ2dnExsba16lTpw5Vq1ZlzZo1AKxZs4aGDRvayxBAx44dSUlJYceOHaWYXkSk8Kw2K2N+HkPXj7uSnJlMTJUYNvXdpDIkUkLKzKBqm83GoEGDaNOmDQ0aNAAgPj4eLy8vQkJC8q0bHh5OfHy8fZ2Ly1De8rxlBcnMzCQzM9P+OCUlpbg+hojIPzqVeopHFjzCkn1LABjQfACvdnwVL/d/3qItIlenzBSi/v37s337dlavXl3i7zVp0iTGjRtX4u8jInKpXw79QtxXcZw4fwJfD19mdp1Jzxt6mh1LxOmViV1mAwYMYOHChfz8889UqVLFPj8iIoKsrCzOnTuXb/2EhAQiIiLs61x61Fne47x1LjVy5EiSk5Pt09GjR4vx04iIXM5qszLul3G0n9ueE+dPULdCXdb+a63KkEgpcehCZBgGAwYM4Ouvv+ann34iOjo63/Ibb7wRT09Pli9fbp+3e/dujhw5QkxMDAAxMTH88ccfJCYm2tdZtmwZQUFB1KtXr8D39fb2JigoKN8kIlJSTpw/QeyHsYxdMRabYePxxo+z/on1NAxvaHY0EZfh0LvM+vfvz/z58/nmm28IDAy0j/kJDg7G19eX4OBg+vTpw5AhQwgNDSUoKIinn36amJgYWrVqBUCHDh2oV68evXr1YsqUKcTHx/PCCy/Qv39/vL29zfx4IiIs3ruYRxY8wum00wR4BTCjywzibogzO5aIy3How+4tFkuB8z/44AMeffRRIPfEjM8++ywff/wxmZmZdOzYkXfffTff7rDDhw/Tr18/fvnlF/z9/enduzeTJ0/Gw6NwfVCH3YtIccuyZvHCTy8w9bepADSOaMyn932qq9SLFKOi/H47dCFyFCpEIlKcdp3aRc+ve7Lp5CYg9yiyqR2m4uPhY3IyEedSlN9vh95lJiLiTGyGjXfWvcOwH4eRkZNBqG8o/+36X+6pe4/Z0URcngqRiEgpOHH+BI998xg/7P8BgI41O/K/u/9HZGCkyclEBFSIRERK3Bc7v+DJhU+SlJ6Ej4cPU2+fSv/m/f92nKSIlD4VIhGREpKckczTi5/mw20fAtC0UlM+uucj6lasa3IyEbmUCpGISAn4fu/39P2uL8fPH8fN4saINiMYc8sYXX5DxEGpEImIFKOz6WcZtHQQc7fOBaBmuZrM7jabtlXbmpxMRK5EhUhEpJh88+c3/HvRv4m/EI8FC4NaDWLCbRPw8/QzO5qI/AMVIhGRa3Q67TTPLH6Gj7d/DEDt8rX5393/o3VUa5OTiUhhqRCJiFwlwzD4fOfnPL34aRJTE3GzuDG09VDG3jJWJ1kUKWNUiERErsKBswcY8P0AFu9bDED9ivX54O4PaF65ucnJRORqqBCJiBRBljWLV397lfErx5ORk4GXuxcj245kZNuReHvogtEiZZUKkYhIIa0+spp/L/w3O07tAOC26NuY3mW6Lsgq4gRUiERE/sGZtDMM/3E4729+H4CKfhV5tcOr9Lyhp842LeIkVIhERP6G1Wblv5v+y/M/Pc+Z9DMA/KvJv3j59pcJ9Q01OZ2IFCcVIhGRAqw6vIpnljzDlvgtQO6g6Rl3ztAJFkWclAqRiMhFjiYfZdiPw/hk+ycAhPiEMP6W8fRr3g8PN/3JFHFW+q9bRATIyMng1d9eZeLqiaRlp2HBQt8b+/LirS9S0b+i2fFEpISpEImISzMMg892fMbI5SM5eO4gAG2rtmVap2k0qdTE5HQiUlpUiETEZa04tIKhy4ay/sR6ACoHVmbq7VPp0aCHjh4TcTEqRCLicnae2snwH4ezcM9CAAK8AhjeZjiDWw3G38vf5HQiYgYVIhFxGSfPn2TML2N4f/P72Awb7hZ3nrzxScbcMoYw/zCz44mIiVSIRMTpJaUnMfXXqUxbN4207DQAutftzsTbJlK7Qm2T04mII1AhEhGnlZyRzOu/v85ra17jfNZ5AGKqxPBKh1doHdXa5HQi4khUiETE6VzIusC0tdN45bdXOJtxFoBG4Y0Yf+t4ul7fVQOmReQyKkQi4jTSstOYvn46k3+dzOm00wDUq1iPcbeMo3vd7rhZ3ExOKCKOSoVIRMq8lMwU3l3/Lq+teY1TaacAuC70Osa2G0uPBj1wd3M3OaGIODoVIhEps06nnebN39/krXVvkZyZDED1kOqMvnk0vRr10qU2RByRYUBiIuzdC3v2/DX5+MD8+abF0l8LESlzjqcc59U1r/LexvfsR43VrVCXkW1H0qNBDzzdPU1OKOLiDANOn84tPfv25b/duxdSUi5/TkhI7vNMGuOnQiQiZcauU7t4/ffXmbN1DlnWLACaRDTh+Zue556692iMkEhpyis9Fxedi8tPcvLfP9digWrV4Prr808qRCIiBTMMg58P/cyra17l+73f2+e3rdqW5296no41O+qoMZGScmnpubT8XKn0AERFQa1acN11+W9r1szdReZAVIhExCFlWbP4ZPsnvLbmNbYmbAXAgoW7at/FszHPclO1m0xOKOIkDANOnYL9+wvexXW1padGDfD1LZ3PUAxUiETEoZxKPcWsTbN4e93bnLxwEgA/Tz8ea/wYA1sOpFb5WiYnFCmDsrLg8GE4cCC3+Fx8e+AAXLhw5ec7Sem5EhUiETGdYRisObaGd9e/y+c7P7ePD6oUUImnWzzNk82eJNQ31OSUIg7MMODs2fwl5+L7R4+Czfb3z7dYoHLl3JKTNzlh6bkSFSIRMU1qVirz/5jPuxveZUv8Fvv85pHNGdBiAD0a9MDL3cu8gCKOJCcHjhy5vPDk3f7Tri0/v9xyU6NG7hiei2+rVXO4MT2lTYVIRErd9sTtzNo4i9lbZ5OSmXv4rY+HDw81eIh+zfrRvHJzkxOKmMAwID4eDh3KP+UVoMOHwWq98mtUqlRw4alRA8LDTTuCqyxQIRKRUpGckcwn2z/h/c3vs/7Eevv8muVq0q9ZPx5r8ph2i4lzyzsh4aFDcPDg5cXn8GHIyLjya3h7/1VwLi080dG5W4HkqqgQiUiJMQyDFYdX8L/N/+OLnV+QnpMOgIebB12v70rfG/vSoWYHnT9InEPe0Vp5Baeg0vNPhcfNDapUgerV8081a+ZOlSrlriPFToVIRIrd3jN7+Xj7x8zdOpf9Z/fb59etUJc+TfrQq1EvwvzDTEwochVsttxdWkeO5E6HD19eetLTr/waFkvBhad69dwtPFWqgKfOtG4GFSIRKRbxF+L5dPunzPtjXr5dYoFegfRo0IM+TfrQonILnURRHNf587lF5+jRv0rPxdOxY5CdfeXXsFggMvKvgnNp6YmKAi8dKOCIVIhE5KqlZKbw9a6vmffHPJYfXI7NyD2s193iTmyNWOIaxtG9bnf8vfxNTiouLycHTp4suOjkTefO/fPruLnlHp4eFZV/y87Fhcfbu0Q/ipQMFSIRKZLkjGQW7lnIV39+xfd7vycj568xEa2qtOLhBg/zQP0HCA8INzGluBTDgDNncrfgHDtW8Bae48f/+QgtyL3AaNWqBU9RUblbfzz00+mM9E9VRP7RqdRTfLv7W77c9SU/HviRbNtfuw3qVKhDXMM4HmrwEDVDa5qYUpyS1Zo7bufYsdxSk1d6Lr2fmfnPr+XhkVtqCio6ebdBQSX/mcQhqRCJSIGOpRxjwZ8L+GrXV6w4vMK+OwxyB0ffW/deutftTuOIxhoXJFcnMxNOnPir2BRUdE6eLNyWHYCwsNzdWX+3hSc8HNzdS/YzSZmlQiQiAFhtVn4/9juL9i5i0d5FbEvYlm95k4gm9hJUt2Jdk1JKmWCz5e7COnkyt/Dk3V5aek6dKtzrubvn7qqqXDn3KKy86eLHlSpp7I5cExUiERd2Ju0MS/cvZdHeRSzZt4Sk9CT7MgsWYqJi6F6nO93rdie6XLSJScUh2Gxw+vTlRefS2/j4fz4aK4+399+XnLzH2rIjpUCFSMSFZFmz+P3Y7/x44Ed+PPAja4+vzbcrLMQnhE7XdaJLrS50uq4TFfwqmJhWSo3Nlru1pjBFJyen8K9boULulp1KlXJvCyo8oaG6nIQ4BBUiESdmM2xsS9jGjwd+ZPnB5aw8vJK07LR86zQMa0iXWl24o9YdxETF4OGmPwtOIe/q5wkJf03x8Zc/zptXlKITFvZXybn49uL7ERE6346UKfrLJ+JErDYrfyT+warDq1h1ZBU/H/qZ02mn860T5h9G++j2xNaIJbZGLFWDq5qUVorMMHLPlVNQsbn0fmIiZGUV/rUtlr8vOhffhofrTMrilFSIRMqwjJwM1h1fx+ojq1l1ZBW/Hf3NfvX4PP6e/rSr3o7Y6NwC1CCsgY4KcyRZWbm7qy6dLi48eUWnqCUHIDg4d2tNePhf08WPIyJyi05YmIqOuDQVIpEywjAMDpw9wPoT61l3fB1rj69lw4kNZFnz/0AGegXSOqo1bau2pV21drSs0hIvd+26KDXp6QUXnMTEguenpPzza14qOPjyYlNQ0QkLAx+f4v+MIk5IhUjEQSWmJrL+eG75WXdiHeuOr8t3FFiecP9wbqp2EzdVvYm2VdtyQ/gNGgdUXKzW3F1UZ878NRVUai6eUlOL/j5ubrkDkMPCoGLFv6a8gnNp8VHJESl2+qspYjKbYePA2QNsjd/K1oT/n+K3cjj58GXrerl70TiiMc0jm9M8sjltqrahZrma2gX2TwwD0tLyF5uLp6SkguefO5f73KLy9MxfbP5pKlcutxSJiGlUiERK0bmMc+w6tYttCdvs5WdbwjYuZF0ocP06FerQonILWkS2oEXlFtwQfgPeHi588jnDyN3FdO5c7hFUebdnz/59qckrPIW5tMPfCQyE8uVzpwoVCi41F2/dCQrSoeQiZYwKkUgxMwyD4+eP8+fpP9l1ahe7Tu/KvX96F/EX4gt8jre7N/XD6tMovFHuFNGIJhFNCPYJLuX0pSA7O3+RKajc/N3yc+dyz5lztTw9/yo2l06hoX8/X4ONRZyeCpHIVbAZNo6nHGf/2f0cOHuA/Un72X82d9p9ejfns87/7XMjAyNpGNbQXnwahTeidoXaZWPcT3Z27haavCk5ueDbvPvJyZeXm6sZY3Mpb+/c3UwhIX/dFqbc+Ptry42IFKgM/AUWKX1Wm5X4C/EcSznG0ZSjHE0+yoGzBzhwLrf8HDx38LKjuy7mbnHnutDrqFOhDnUr1M29rZh7G+RdylfTNozc3UXnz8OFC7nT+fOXl5crFZu82/T04ssVFJS/0JQrV7j7ISHg61t8OUREUCESF2MYBimZKSSkJpBwIYGE1ASOpRyzF59jKcc4mnyUE+dPYDWufIVtDzcPqodUp2a5mtQoV8N+W7tCba4Lve7qDnU3DMjI+Ku4XFxgCjPv7+YX9mrhheXrm1togoPz3xY0r6BiExwMHvrzIyKOw6X+Ir3zzjtMnTqV+Ph4GjVqxFtvvUWLFi3MjiXXwGqzkpyZzNn0sySlJ5GUnsTZjLMkpibaC8/F5SfhQgKZ1sINrnW3uBMZGElUcBRVgqoQHVydmgFR1PCpRE3PcKpYgvHIyMo9eiktDRLS4GAapP0Kacv+ml+UKTW1+MvLxXx9ISAgd5DwlUpMQfPybgMDdUkGEXE6LlOIPv30U4YMGcKMGTNo2bIlb7zxBh07dmT37t2EhYWZHc/lGIZBek46F7IucD7zfO5t1vl89+3LMlNITjtLUtoZzqYnkZRxlqSMs5zNTOZcVgoGRT8sOsDmSbjVh/BsLypnehOV7kmVNA+izrsRlQJVztmISLbinp4JGbsgfVPulpvS5OeXW14ungIDL5/3d/MvnefvryuGi4j8DYthXM1JNsqeli1b0rx5c95++20AbDYbUVFRPP3004wYMeKKz01JSSE4OJjk5GSCgopv/Ic1O4tje9ZjGAaGzYZh5E3//9hmxTAMMIz8ywzbX+v//xE3f80z7M+7eH0ufk1rDobVimG1YrVmk23NIseaTU5ONtm2LHKsObmPrdlk2/7/vi2HbFvubY4th2wj9zbLlk26kU3GJVM62WQYOWSQTQZWMiw5pFusZFispLtZueBuxVaMY1v9syA0HcqlQ7kMqJgK4akQfqHgW7/sa3xDH5/cwlISU2CgyouISDEoyu+3S2whysrKYuPGjYwcOdI+z83NjdjYWNasWXPZ+pmZmWRedM6SlKs5tX4hJB7ZSfUv2pbIa5cKC1AMv9n+WRCYCYFZEPD/9wOych/n3Q/KzC08oelQLsuN0GwPyuV4EGr4Us7ii5eXb25J8fbOvc2bKnpD1EWPL11emMfe3vkLi6+vTqInIuJkXKIQnT59GqvVSnh4eL754eHh/Pnnn5etP2nSJMaNG1fiudzcPPD5/y0VFsBi/P9tvvsWLMZF6xSwLG9Dy6Xz/lrfkn+55f9vseBhWPDAgqfhhofhhgdueJJ7+9d9dzwsbnjijofF/f9v3fCwuONt8cTH4oGPxQsfiye+bl74uHnh4+6Dj4c3Ph6++Hj44Ovpi4+nLz5efvh4+RHgHUigTxD+3oG4efvknufFyyt3yrt/6W3efZUREREpZi5RiIpq5MiRDBkyxP44JSWFqKioYn+f8OgGpE9wiT2WIiIiDs0lClGFChVwd3cnISEh3/yEhAQiIiIuW9/b2xtvbxe+PIKIiIiLcYl9D15eXtx4440sX77cPs9ms7F8+XJiYmJMTCYiIiKOwCW2EAEMGTKE3r1706xZM1q0aMEbb7xBamoqjz32mNnRRERExGQuU4gefPBBTp06xejRo4mPj6dx48YsWbLksoHWIiIi4npc5jxE16KkzkMkIiIiJacov98uMYZIRERE5EpUiERERMTlqRCJiIiIy1MhEhEREZenQiQiIiIuT4VIREREXJ4KkYiIiLg8FSIRERFxeSpEIiIi4vJc5tId1yLvZN4pKSkmJxEREZHCyvvdLsxFOVSICuH8+fMAREVFmZxEREREiur8+fMEBwdfcR1dy6wQbDYbJ06cIDAwEIvFUqyvnZKSQlRUFEePHtV10kqQvufSoe+5dOh7Lj36rktHSX3PhmFw/vx5IiMjcXO78ighbSEqBDc3N6pUqVKi7xEUFKT/2EqBvufSoe+5dOh7Lj36rktHSXzP/7RlKI8GVYuIiIjLUyESERERl6dCZDJvb2/GjBmDt7e32VGcmr7n0qHvuXToey49+q5LhyN8zxpULSIiIi5PW4hERETE5akQiYiIiMtTIRIRERGXp0IkIiIiLk+FyETvvPMO1atXx8fHh5YtW7Ju3TqzIzmdSZMm0bx5cwIDAwkLC6Nbt27s3r3b7FhOb/LkyVgsFgYNGmR2FKdz/PhxevbsSfny5fH19aVhw4Zs2LDB7FhOxWq1MmrUKKKjo/H19aVmzZq8+OKLhboellzZypUr6dq1K5GRkVgsFhYsWJBvuWEYjB49mkqVKuHr60tsbCx79+4tlWwqRCb59NNPGTJkCGPGjGHTpk00atSIjh07kpiYaHY0p7JixQr69+/P77//zrJly8jOzqZDhw6kpqaaHc1prV+/nvfee48bbrjB7ChO5+zZs7Rp0wZPT08WL17Mzp07efXVVylXrpzZ0ZzKyy+/zPTp03n77bfZtWsXL7/8MlOmTOGtt94yO1qZl5qaSqNGjXjnnXcKXD5lyhSmTZvGjBkzWLt2Lf7+/nTs2JGMjIySD2eIKVq0aGH079/f/thqtRqRkZHGpEmTTEzl/BITEw3AWLFihdlRnNL58+eNWrVqGcuWLTPatWtnDBw40OxITmX48OFG27ZtzY7h9Lp06WI8/vjj+eZ1797diIuLMymRcwKMr7/+2v7YZrMZERERxtSpU+3zzp07Z3h7exsff/xxiefRFiITZGVlsXHjRmJjY+3z3NzciI2NZc2aNSYmc37JyckAhIaGmpzEOfXv358uXbrk+3dbis+3335Ls2bNuP/++wkLC6NJkybMmjXL7FhOp3Xr1ixfvpw9e/YAsHXrVlavXk3nzp1NTubcDh48SHx8fL6/H8HBwbRs2bJUfht1cVcTnD59GqvVSnh4eL754eHh/Pnnnyalcn42m41BgwbRpk0bGjRoYHYcp/PJJ5+wadMm1q9fb3YUp3XgwAGmT5/OkCFD+M9//sP69et55pln8PLyonfv3mbHcxojRowgJSWFOnXq4O7ujtVq5aWXXiIuLs7saE4tPj4eoMDfxrxlJUmFSFxG//792b59O6tXrzY7itM5evQoAwcOZNmyZfj4+Jgdx2nZbDaaNWvGxIkTAWjSpAnbt29nxowZKkTF6LPPPmPevHnMnz+f+vXrs2XLFgYNGkRkZKS+ZyemXWYmqFChAu7u7iQkJOSbn5CQQEREhEmpnNuAAQNYuHAhP//8M1WqVDE7jtPZuHEjiYmJNG3aFA8PDzw8PFixYgXTpk3Dw8MDq9VqdkSnUKlSJerVq5dvXt26dTly5IhJiZzT0KFDGTFiBD169KBhw4b06tWLwYMHM2nSJLOjObW83z+zfhtViEzg5eXFjTfeyPLly+3zbDYby5cvJyYmxsRkzscwDAYMGMDXX3/NTz/9RHR0tNmRnFL79u35448/2LJli31q1qwZcXFxbNmyBXd3d7MjOoU2bdpcdtqIPXv2UK1aNZMSOae0tDTc3PL/PLq7u2Oz2UxK5Bqio6OJiIjI99uYkpLC2rVrS+W3UbvMTDJkyBB69+5Ns2bNaNGiBW+88Qapqak89thjZkdzKv3792f+/Pl88803BAYG2vdDBwcH4+vra3I65xEYGHjZuCx/f3/Kly+v8VrFaPDgwbRu3ZqJEyfywAMPsG7dOmbOnMnMmTPNjuZUunbtyksvvUTVqlWpX78+mzdv5rXXXuPxxx83O1qZd+HCBfbt22d/fPDgQbZs2UJoaChVq1Zl0KBBTJgwgVq1ahEdHc2oUaOIjIykW7duJR+uxI9jk7/11ltvGVWrVjW8vLyMFi1aGL///rvZkZwOUOD0wQcfmB3N6emw+5Lx3XffGQ0aNDC8vb2NOnXqGDNnzjQ7ktNJSUkxBg4caFStWtXw8fExatSoYTz//PNGZmam2dHKvJ9//rnAv8m9e/c2DCP30PtRo0YZ4eHhhre3t9G+fXtj9+7dpZLNYhg69aaIiIi4No0hEhEREZenQiQiIiIuT4VIREREXJ4KkYiIiLg8FSIRERFxeSpEIiIi4vJUiERERMTlqRCJiMs4dOgQFouFLVu2mB1FRByMCpGIuIyoqChOnjxZ6MuJWK1WWrduTffu3fPNT05OJioqiueff74kYoqICXSmahGRK9izZw+NGzdm1qxZxMXFAfDII4+wdetW1q9fj5eXl8kJRaQ4aAuRiDgVm83GpEmTiI6OxtfXl0aNGvHFF18ABe8y2759O507dyYgIIDw8HB69erF6dOn7cuvv/56Jk+ezNNPP83Jkyf55ptv+OSTT5g7d67KkIgTUSESEacyadIk5s6dy4wZM9ixYweDBw+mZ8+erFix4rJ1z507x2233UaTJk3YsGEDS5YsISEhgQceeCDfek8//TSNGjWiV69e9O3bl9GjR9OoUaPS+kgiUgq0y0xEnEZmZiahoaH8+OOPxMTE2Of/61//Ii0tjYkTJxIdHc3mzZtp3LgxEyZMYNWqVSxdutS+7rFjx4iKimL37t1cf/319vl//vkndevWpWHDhmzatAkPD49S/WwiUrL0X7SIOI19+/aRlpbG7bffnm9+VlYWTZo0uWz9rVu38vPPPxMQEHDZsv379+crRP/73//w8/Pj4MGDHDt2jOrVqxd7fhExjwqRiDiNCxcuALBo0SIqV66cb5m3tzdWq/Wy9bt27crLL7982WtVqlTJfv+3337j9ddf54cffmDChAn06dOHH3/8EYvFUgKfQkTMoEIkIk6jXr16eHt7c+TIEdq1a3fZ8kOHDuV73LRpU7788kuqV6/+t7vA0tLSePTRR+nXrx+33nor0dHRNGzYkBkzZtCvX7+S+BgiYgINqhYRpxEYGMhzzz3H4MGDmTNnDvv372fTpk289dZbzJkz57L1+/fvT1JSEg899BDr169n//79LF26lMcee8y+NWnkyJEYhsHkyZMBqF69Oq+88grDhg27rGCJSNmlQdUi4lQMw2DatGlMnz6dAwcOEBISQtOmTfnPf/5D1apV8w2qBti7dy/Dhw/n559/JjMzk2rVqtGpUydee+01Vq5cSfv27fnll19o27Ztvvfp2LEjOTk52nUm4iRUiETEZezevZs6deqwd+9errvuOrPjiIgD0S4zEXEJSUlJfPHFFwQFBREVFWV2HBFxMBpULSIuoU+fPmzcuJHp06fj7e1tdhwRcTDaZSYiIiIuT7vMRERExOWpEImIiIjLUyESERERl6dCJCIiIi5PhUhERERcngqRiIiIuDwVIhEREXF5KkQiIiLi8lSIRERExOX9HxR9yKy78bQ+AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_eRgvwBmNFgi"
      },
      "source": [
        "Para ver implementaciones más complejas les dejo dos links que visito harto:\n",
        "\n",
        "\n",
        "\n",
        "*   [¿Como tener dos rangos distintos en eje y?](https://cmdlinetips.com/2019/10/how-to-make-a-plot-with-two-different-y-axis-in-python-with-matplotlib/)\n",
        "*   [Mi biblia para plots](https://cmdlinetips.com/2019/10/how-to-make-a-plot-with-two-different-y-axis-in-python-with-matplotlib/)\n",
        "\n",
        "Para todo lo demás está Stackoverflow y Chat GPT\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Plotly\n",
        "\n",
        "Una librería que se está utilizando mucho por sus funcionalidades e interactividad es la librería plotly. La cual nos permite transformar los gráficos anteriores"
      ],
      "metadata": {
        "id": "DvhXOy_9Ad3z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "px.line(x=x,y=y)"
      ],
      "metadata": {
        "id": "Gkl9cOQwAoDQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "a5fced96-74b8-4594-c068-cd3f5feba895"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMYPEZy_OOsL"
      },
      "source": [
        "###4.3 Pandas\n",
        "\n",
        "Es LA librería de Python especializada en el manejo y análisis de estructuras de datos. Por ende a lo largo del semestre la usaremos bastaaante por lo que a continuación veremos SOLO una pincelada de las caracteristicas generales. No obstante en la próxima aux la veremos muuuucho más en detalle\n",
        "\n",
        "\n",
        "HYPE MODE ON"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7HJEDHA2Ossr"
      },
      "source": [
        "####Series"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNAL91bpMM_n"
      },
      "source": [
        "# importar las librerias a utilizar\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pXBL7Ww_MNEy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63e6c06f-1ea7-4477-eb54-959a91e1c3a9"
      },
      "source": [
        "etiquetas = ['a', 'b', 'c']\n",
        "datos = [1,2,3]\n",
        "serie_1 = pd.Series(data = datos, index  = etiquetas)\n",
        "serie_1"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    1\n",
              "b    2\n",
              "c    3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgLE-ntHMNHx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44171636-3ec6-41c5-f916-6769bc429768"
      },
      "source": [
        "serie_1['a']"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zeT36IPUMNKd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cef506de-0a06-4f2b-a8b2-181cb3cacdd3"
      },
      "source": [
        "array = np.arange(5,8)\n",
        "etiqueta = ['venta1', 'venta2', 'venta3']\n",
        "serie1 = pd.Series(array, etiqueta)\n",
        "serie1"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "venta1    5\n",
              "venta2    6\n",
              "venta3    7\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0P_adxoBMNS3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc55575a-444d-4da1-ae93-23341d064237"
      },
      "source": [
        "serie1['venta1']"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sP-voxLTMNWA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a2d35e2-8e92-44b6-9dae-15938abc2283"
      },
      "source": [
        "serie1[0]"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JvjOk5daMNYx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35facc56-b729-4daa-a591-9dbd610008c8"
      },
      "source": [
        "datos2 = ['informatica', 300, 'impresora', 400]\n",
        "pd.Series(datos2)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    informatica\n",
              "1            300\n",
              "2      impresora\n",
              "3            400\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIDIVLoiMNbr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80797f53-a521-4d41-9929-7e1fc9a9619d"
      },
      "source": [
        "serie1= pd.Series([1,2,3,4], ['ventas1','ventas2','ventas3', 'ventas4'])\n",
        "serie2= pd.Series([4,8,5], ['ventas1','ventas2','ventas3'])\n",
        "serie_suma = serie1 + serie2\n",
        "serie_suma"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ventas1     5.0\n",
              "ventas2    10.0\n",
              "ventas3     8.0\n",
              "ventas4     NaN\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rSimuZTlOv1J"
      },
      "source": [
        "####Dataframes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUtDT0acMNeO"
      },
      "source": [
        "#Podemos crear nuestro DF\n",
        "#Opcion 1\n",
        "filas = ['ventas1', 'ventas2', 'ventas 3']\n",
        "columnas = ['zonaA', 'zonaB', 'zonaC']\n",
        "datos = [[123,421,256], [234,541,257], [120,451,258]]\n",
        "\n",
        "dataframe = pd.DataFrame(datos, filas, columnas)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNxvhAdlLVZT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "960a4daa-5189-49f8-dee9-abc29595ced5"
      },
      "source": [
        "#Opcion 2\n",
        "dic_ventas = {\n",
        "    'zonaA':[123,421,256],\n",
        "    'zonaB':[234,541,257],\n",
        "    'zonaC':[120,451,258]\n",
        "}\n",
        "\n",
        "dataframe_2 = pd.DataFrame(data=dic_ventas,index=filas)\n",
        "dataframe_2"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          zonaA  zonaB  zonaC\n",
              "ventas1     123    234    120\n",
              "ventas2     421    541    451\n",
              "ventas 3    256    257    258"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-cd7db010-ac2d-4109-8628-07b96019d9ae\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>zonaA</th>\n",
              "      <th>zonaB</th>\n",
              "      <th>zonaC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ventas1</th>\n",
              "      <td>123</td>\n",
              "      <td>234</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ventas2</th>\n",
              "      <td>421</td>\n",
              "      <td>541</td>\n",
              "      <td>451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ventas 3</th>\n",
              "      <td>256</td>\n",
              "      <td>257</td>\n",
              "      <td>258</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-9f60914f-b3f2-4351-91a2-0f28aece3c30\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9f60914f-b3f2-4351-91a2-0f28aece3c30')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-9f60914f-b3f2-4351-91a2-0f28aece3c30 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cd7db010-ac2d-4109-8628-07b96019d9ae button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cd7db010-ac2d-4109-8628-07b96019d9ae');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jg9Ql9_tPmHB"
      },
      "source": [
        "\n",
        "\n",
        "También podemos llamar un dataset de un archivo csv, excel u otros formatos con el comando\n",
        "\n",
        "```\n",
        "df = pd.read_csv('Archivo_i.csv')\n",
        "```\n",
        "o\n",
        "```\n",
        "df = pd.read_xlsx('Archivo_i.xlsx')\n",
        "```\n",
        "\n",
        "Sin embargo, esas implementaciones las veremos en detalle más adelante."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kag0uAMPAaN"
      },
      "source": [
        "#####Operaciones sobre df"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7g1c0RWLVbq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "fc7f7c51-2c6a-4309-fc3b-a575350653ec"
      },
      "source": [
        "#Seleccionar\n",
        "dataframe.loc[['ventas1', 'ventas2']]"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         zonaA  zonaB  zonaC\n",
              "ventas1    123    421    256\n",
              "ventas2    234    541    257"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-d123be67-fec0-4a78-86df-b6e725ce6955\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>zonaA</th>\n",
              "      <th>zonaB</th>\n",
              "      <th>zonaC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ventas1</th>\n",
              "      <td>123</td>\n",
              "      <td>421</td>\n",
              "      <td>256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ventas2</th>\n",
              "      <td>234</td>\n",
              "      <td>541</td>\n",
              "      <td>257</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d123be67-fec0-4a78-86df-b6e725ce6955')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-2e90f806-3efd-480d-b5df-52e88df03fcd\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2e90f806-3efd-480d-b5df-52e88df03fcd')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-2e90f806-3efd-480d-b5df-52e88df03fcd button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d123be67-fec0-4a78-86df-b6e725ce6955 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d123be67-fec0-4a78-86df-b6e725ce6955');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_DIy4YgPIr6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "58720233-1df5-45d9-a871-1762fca9dadb"
      },
      "source": [
        "dataframe[['zonaA', 'zonaC']]"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          zonaA  zonaC\n",
              "ventas1     123    256\n",
              "ventas2     234    257\n",
              "ventas 3    120    258"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-815775cc-6123-47ee-a60c-e926d9d58c7b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>zonaA</th>\n",
              "      <th>zonaC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ventas1</th>\n",
              "      <td>123</td>\n",
              "      <td>256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ventas2</th>\n",
              "      <td>234</td>\n",
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              "      <td>258</td>\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-815775cc-6123-47ee-a60c-e926d9d58c7b button.colab-df-convert');\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-815775cc-6123-47ee-a60c-e926d9d58c7b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7NJZsKGPIup",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73ee22ab-ead3-48da-a8f6-722d21c87f4f"
      },
      "source": [
        "dataframe.loc['ventas1','zonaA']"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "123"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oaFGZE3MPIxV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "e5db2b9d-3434-4d37-8078-717c13054ff7"
      },
      "source": [
        "dataframe"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          zonaA  zonaB  zonaC\n",
              "ventas1     123    421    256\n",
              "ventas2     234    541    257\n",
              "ventas 3    120    451    258"
            ],
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69cF_qdsPI0Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "2a7b0d5c-89da-4a92-f3e1-22295baca819"
      },
      "source": [
        "#Crear nuevas columnas\n",
        "dataframe['VentasTotales'] = [0,0,0]\n",
        "dataframe"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          zonaA  zonaB  zonaC  VentasTotales\n",
              "ventas1     123    421    256              0\n",
              "ventas2     234    541    257              0\n",
              "ventas 3    120    451    258              0"
            ],
            "text/html": [
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              "\n",
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              "      <td>257</td>\n",
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              "      <th>ventas 3</th>\n",
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              "\n",
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              "    <div id=\"df-cc29d7f6-7282-4f99-93c2-b18db66574d6\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cc29d7f6-7282-4f99-93c2-b18db66574d6')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-cc29d7f6-7282-4f99-93c2-b18db66574d6 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-16f831bb-a81a-42d8-be5f-434fb0599068 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-16f831bb-a81a-42d8-be5f-434fb0599068');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xx-oqjMBPPuR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "d48dc580-6558-4f89-a06f-a03147662941"
      },
      "source": [
        "dataframe['VentasHoy'] = 0\n",
        "dataframe"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          zonaA  zonaB  zonaC  VentasTotales  VentasHoy\n",
              "ventas1     123    421    256              0          0\n",
              "ventas2     234    541    257              0          0\n",
              "ventas 3    120    451    258              0          0"
            ],
            "text/html": [
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              "\n",
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              "      <th>zonaA</th>\n",
              "      <th>zonaB</th>\n",
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              "      <th>VentasTotales</th>\n",
              "      <th>VentasHoy</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ventas1</th>\n",
              "      <td>123</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>234</td>\n",
              "      <td>541</td>\n",
              "      <td>257</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ventas 3</th>\n",
              "      <td>120</td>\n",
              "      <td>451</td>\n",
              "      <td>258</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d82cbcd7-b946-4cab-a0d1-decdfe1449f5')\"\n",
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              "    </g>\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "      <script>\n",
              "\n",
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              "        displayQuickchartButton(document);\n",
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              "      <style>\n",
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              "      display:flex;\n",
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              "      fill: #174EA6;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d82cbcd7-b946-4cab-a0d1-decdfe1449f5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d82cbcd7-b946-4cab-a0d1-decdfe1449f5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zqd8KecVPI2s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "b4fb81f2-3ed2-45ba-ca88-f2dda7dc7a10"
      },
      "source": [
        "#Sumar columnas\n",
        "dataframe.loc[:,'VentasTotales'] = dataframe.loc[:,'zonaA']+dataframe.loc[:,'zonaB']+dataframe.loc[:,'zonaC']\n",
        "dataframe"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          zonaA  zonaB  zonaC  VentasTotales  VentasHoy\n",
              "ventas1     123    421    256            800          0\n",
              "ventas2     234    541    257           1032          0\n",
              "ventas 3    120    451    258            829          0"
            ],
            "text/html": [
              "\n",
              "\n",
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              "  <thead>\n",
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              "      <th>VentasHoy</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ventas1</th>\n",
              "      <td>123</td>\n",
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              "      <td>800</td>\n",
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              "      <th>ventas2</th>\n",
              "      <td>234</td>\n",
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              "      <td>1032</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ventas 3</th>\n",
              "      <td>120</td>\n",
              "      <td>451</td>\n",
              "      <td>258</td>\n",
              "      <td>829</td>\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-e3d38956-3d02-424f-a7d3-dee2db0633b3 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8fc65bb9-460b-40fe-b2a2-f48a84a689b5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8fc65bb9-460b-40fe-b2a2-f48a84a689b5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZ4DaeKbPI4_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "5df0c965-e183-4986-9239-c76221846ddb"
      },
      "source": [
        "#Botar columnas\n",
        "dataframe.drop('VentasTotales', axis=1, inplace = True)\n",
        "dataframe"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          zonaA  zonaB  zonaC  VentasHoy\n",
              "ventas1     123    421    256          0\n",
              "ventas2     234    541    257          0\n",
              "ventas 3    120    451    258          0"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-44ab7bd8-fb49-4ac0-9eb0-078768a42840\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>zonaA</th>\n",
              "      <th>zonaB</th>\n",
              "      <th>zonaC</th>\n",
              "      <th>VentasHoy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ventas1</th>\n",
              "      <td>123</td>\n",
              "      <td>421</td>\n",
              "      <td>256</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ventas2</th>\n",
              "      <td>234</td>\n",
              "      <td>541</td>\n",
              "      <td>257</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ventas 3</th>\n",
              "      <td>120</td>\n",
              "      <td>451</td>\n",
              "      <td>258</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44ab7bd8-fb49-4ac0-9eb0-078768a42840')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-42049c66-b641-440b-9d0b-7354e757a0d8\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-42049c66-b641-440b-9d0b-7354e757a0d8')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-42049c66-b641-440b-9d0b-7354e757a0d8 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-44ab7bd8-fb49-4ac0-9eb0-078768a42840 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-44ab7bd8-fb49-4ac0-9eb0-078768a42840');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBjQnSzGPe6B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "9c108286-328b-4e1d-cc86-01c9740e37f9"
      },
      "source": [
        "dataframe.drop('ventas2',inplace = True)\n",
        "dataframe"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          zonaA  zonaB  zonaC  VentasHoy\n",
              "ventas1     123    421    256          0\n",
              "ventas 3    120    451    258          0"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-183be911-f764-4735-9525-e356bec036a4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>zonaA</th>\n",
              "      <th>zonaB</th>\n",
              "      <th>zonaC</th>\n",
              "      <th>VentasHoy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ventas1</th>\n",
              "      <td>123</td>\n",
              "      <td>421</td>\n",
              "      <td>256</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ventas 3</th>\n",
              "      <td>120</td>\n",
              "      <td>451</td>\n",
              "      <td>258</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-183be911-f764-4735-9525-e356bec036a4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-2062d85f-f023-43db-8187-3c03d955d216\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2062d85f-f023-43db-8187-3c03d955d216')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-2062d85f-f023-43db-8187-3c03d955d216 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-183be911-f764-4735-9525-e356bec036a4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-183be911-f764-4735-9525-e356bec036a4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znKaKithPe8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e31a2c0-2d2b-4bd0-e33d-dcdd64c46221"
      },
      "source": [
        "dataframe.shape"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cSNVNqebPe_h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "95318116-df65-4a18-956e-eea419663a65"
      },
      "source": [
        "#Podemos aplicar condiciones al igual que en numpy\n",
        "dataframe.loc[dataframe.loc[:,'zonaA']>200]"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [zonaA, zonaB, zonaC, VentasHoy]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-16eecc64-69a4-4dcc-adb5-f3a10061ed3f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>zonaA</th>\n",
              "      <th>zonaB</th>\n",
              "      <th>zonaC</th>\n",
              "      <th>VentasHoy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16eecc64-69a4-4dcc-adb5-f3a10061ed3f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-d092b7f2-55d4-4790-8477-b64d916b97dd\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d092b7f2-55d4-4790-8477-b64d916b97dd')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-d092b7f2-55d4-4790-8477-b64d916b97dd button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-16eecc64-69a4-4dcc-adb5-f3a10061ed3f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-16eecc64-69a4-4dcc-adb5-f3a10061ed3f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    }
  ]
}