GoogleColaboratory.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GoogleColaboratory.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Google Colaboratory\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/FerneyOAmaya/Competencias-Digitales/blob/master/GoogleColaboratory.ipynb)\n",
        "\n",
        "Se presenta un breve tutorial para el uso de la herramienta Google Colaboratory o \"Colab\". Colab permite escribir y ejecutar código en el lenguaje de programación Python.\n",
        "\n",
        "Por:\n",
        "\n",
        "Ferney Amaya Fernández\n",
        "https://ferneyoamaya.github.io/\n",
        "\n",
        "Contenido:\n",
        "1. Celdas de Texto y ecuaciones.\n",
        "2. Celdas de código y lenguaje Python.\n",
        "3. Bilbioteca Numpy.\n",
        "4. Gráficas empleando Matplotlib.\n",
        "5. Biblioteca Pandas.\n",
        "6. Open IA.\n",
        "<br>¿Dónde encontrar más información?"
      ],
      "metadata": {
        "id": "9o_pO6ho4rjP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.Celdas de texto y ecuaciones\n",
        "\n",
        "Colab tiene dos tipos de celdas: Código y Texto. Las celdas de Texto reciben código en un formato llamado Markdown. A continuación se presentan ejemplos:\n",
        "\n",
        "\n",
        "\n",
        "**negrita**\n",
        "\n",
        "_cursiva_ o *cursiva*\n",
        "\n",
        "~~tachado~~\n",
        "\n",
        ">Texto tabulado\n",
        "\n",
        "`ejemplo de código print(a)`\n",
        "\n",
        "[Enlace a UPB](https://www.upb.edu.co)\n",
        "\n",
        "Lista no enumerada:\n",
        "- primer item\n",
        "- segundo item\n",
        "- tercer item\n",
        "\n",
        "Tabla:\n",
        "\n",
        "|Columna 1|Columna 2|\n",
        "|---------|---------|\n",
        "|   A     |    B    | \n",
        "|   C     |    D    | \n",
        "\n",
        "Ecuación:\n",
        "\n",
        "$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$\n",
        "\n",
        "\n",
        "En el siguiente enlace se encuentran más formas para codificar texto y ecuaciones:\n",
        "\n",
        "https://colab.research.google.com/notebooks/markdown_guide.ipynb"
      ],
      "metadata": {
        "id": "FEZlmXRbGJXP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2.Celdas de código y lenguaje Python\n",
        "\n",
        "Características básicas:\n",
        "- Se emplea `=` para asignar un valor a una variable.\n",
        "- Se emplea la función `input()` para permitir que el usuario entre datos.\n",
        "- Se emplea la función `print()` para presentar información en pantalla.\n",
        "\n",
        "Ejecutar la siguiente celda:\n"
      ],
      "metadata": {
        "id": "DIiq91g4O4Zi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "var1 = int(input('Entre un número entero: '))\n",
        "var2 = 10\n",
        "var3 = var1 + var2\n",
        "print(var3)"
      ],
      "metadata": {
        "id": "f9JYd4njRFas"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3.Biblioteca Numpy\n",
        "\n",
        "NumPy es una biblioteca para el lenguaje de programación Python que ofrece diversas funciones matemáticas. Enlace:\n",
        "\n",
        "https://numpy.org/\n",
        "\n",
        "Algunas ejemplos empleando funciones de la biblioteca Numpy:"
      ],
      "metadata": {
        "id": "k4KUT0NZRxG5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "v1 = np.array([5, 2, 3, 5, 6])\n",
        "v2 = np.arange(0, 10, 2)\n",
        "v3 = np.random.randint(10, size=10)\n",
        "print('Arreglo Numpy a partir de lista: ', v1)\n",
        "print('Arreglo Numpy seleccionando rango: ', v2)\n",
        "print('Valores aleatorios empleando Numpy: ', v3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QpRvYFjeVhnr",
        "outputId": "fdbe0a53-0977-46be-d144-acb70f481816"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Arreglo Numpy a partir de lista:  [5 2 3 5 6]\n",
            "Arreglo Numpy seleccionando rango:  [0 2 4 6 8]\n",
            "Valores aleatorios empleando Numpy:  [5 8 7 7 8 4 1 2 0 8]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##4.Gráficas empleando Matplotlib\n",
        "\n",
        "Matplotlib es una biblioteca para generar gráficos empleando el lenguaje de programación Python.\n",
        "\n",
        "Se presenta un ejemplo de como realizar una gráfica:"
      ],
      "metadata": {
        "id": "Mi0_LoO9WR4L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.arange(0,10,2)\n",
        "y1 = 2*x + -1\n",
        "y2 = 1.5*x + -5\n",
        "\n",
        "f, ax = plt.subplots(1, 2, figsize=(10, 6))\n",
        "ax[0].axis([0, 10, -50, 50])\n",
        "ax[0].set_xlabel('Etiqueta Eje x', fontsize=14)\n",
        "ax[0].set_ylabel('Etiqueta Eje y', fontsize=14)\n",
        "ax[0].set_title('Título de la Figura.', fontsize=14)\n",
        "ax[0].plot(x,y1)\n",
        "ax[0].plot(x,y2)\n",
        "\n",
        "ax[1].axis([0, 10, -50, 50])\n",
        "ax[1].set_xlabel('Etiqueta Eje x', fontsize=14)\n",
        "ax[1].set_ylabel('Etiqueta Eje y', fontsize=14)\n",
        "ax[1].set_title('Título de la Figura.', fontsize=14)\n",
        "ax[1].plot(x,y2)\n",
        "\n",
        "f.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "BmF2eCONGPPF",
        "outputId": "8f19240a-d551-4028-cbce-803e1cecacdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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e/3eRzqCtLbF2276OXmy/7NY89zVXZM1zL19o81xJ0tCwgJO6SSmxfseBvBfbLu7ctIcnD2XNcxdOn8hLL5nDlec0c8WCaTRNGlvlaCVJw5EFnIa99ua57b3Ybitpnjtnynh+Z/FMVrY2sXJhM7MabZ4rSaq+sgu4iHgz8BZgAbA0pbQx72a+MaX09cEKUBoMvTXPvfKcJla1Zg10bZ5bP8xhkupJuY183wncBHwM+GjJoS3AWwGTn2pae/Pc2/OCbaPNc4cVc5ikelPuCNybgDemlL4XER8q2X8PsKTyYUkDs/fwcX65aU9HL7YHtndtnvvqK+axsrWJRbMabJ47PJjDJNWVcgu4s4HVPew/DoyvXDhS/5ypee6Ll7WwqtXmucOYOUxSXSm3gNsIXAp0b4b5AmBtRSOSytDRPHfjbm7fsIvfPPokx0/aPFenZQ6TVFfKLeA+AXw6IiYAAayMiOvI7im5YbCCk9qdONnGfVv2dvRi69489/eftpBVrU085Wyb56pH5jBJdaXcpbS+GBGjgA8DE4B/ArYCb08pfW0Q49MwVdo89/aNu/nlpj0cOJqtP27zXPWVOUxSvSl7qCKl9FngsxHRDIxIKe0YvLA03KSU2LAzb567fjd3bNrdrXluC6tam1mxsIlpE8dUOVoVkTlMUj3p87WmlNKuwQhEw0tKiUf3HOa2fAH42zfuZuf+rs1zV7U2s7K1iZkNNs9V5ZjDJNWD0xZwEXEf8IyU0hMRcT+QenmfA2QzvP4qpfRohWNUndi+9wi3b9zFbeuzBrpdmue2NrHS5rmqIHOYpHrW2wjcN4Gj+fNvnOF9xgJXAV8GnlGBuFQHdh84yh0bO3uxtTfPnTphNCtsnjv0Th6HnQ/Ctnth+32w7T4YMRJe991qRzZYzGFSnUgpsX3fEVZv2cearXtZs3Ufa7bs5dY3XM45MyZXO7yqOG0Bl1L6YE/PTyciWoE1FYpLBdRb89zL8+a5q1qbuWDWZJvnDrZjh+DxNbD93qxQ23Yv7FgHJ/N6ZvQEmHUhzL60unEOInOYVExtbYnNuw9mRdrWzoJtz8FjAETAwuaJLJ8/rcqRVlev98BFxHnAwymlHi89RMRo4GkppZ+mlDZExMzBCFK1qb15brY81S7u79Y89yUXt7Byoc1zB93hJztH1LbnxdquhyC1ZcfHT4VZF8EVN8Lsi7PnTa3Z6FudM4dJte34yTYefvxA56ja1r2s3bqPg8dOAjB6ZHDezMk8Z9EMls5pZElLAxfMamDiWNtFnek3sA6YDewAiIhHyJJdezPMacCPgJEAKaW9gxSnasDRE1nz3Ns22Dy3avY/nl8CvTf7c9t98GRJb9rJLTD7Ilj0Epi9LHveeFb2lXV4ModJNeLwsZOs276v4/Lnmq37eHD7fo6dzL5sThgzkkWzG3jFU+aypKWRxS0NnDdzMmNGOQDQkzMVcN2z/lTyRNfLOaoTNs+topTgic2dI2vt960deLzznGkLoeUSeMrrskJt1jKYNL1aEdcqc5hUBXsPHWfNtmw0bXVerG3YeYC2fCx8yoTRLGlp4PVXzmdxSwNLWhpZ0DyRkd5eU7ZK/F+3t5ldKpD25rl3bMxmido8d4icPAG7H+56CXT7fXAkHwyKkTD9Amh9djaqNusimLUUxjVWN+76YQ6TBmDHviOsKSnU1mzby6N7Dnccn9UwjiUtDTz/wtksaWlg6ZxGWhrHOXltgBw2GcZsnlsFx4/AjrWdhdq2+7LJBifyZDdqHMxcAkte3nkJdMZiGO1665Kqq71/55qte1ndcc/avo4engDzmyZw0dwpvOryeSxpye5Za540topR168zFXAJmBoRJ0q2p0RE+9SP4T0FpGBsnjvEju6H7fd3vQS68wFoy/85jW3IRtOW35BfAr0Ims+DkX6vqiBzmNQPJ062sXHXwaxY29I5uWDfkeyf0sgRwbkzJvH0c6ezpKWBJS0NLG5pYLJXZ4ZMOffAre22fVe3bS8/1LDte490tPXo3jx3VWtT/rB57oAd3FXSXy0fWduzofP4xBlZkXbe87JCbfZFMGU+jPDm3EFmDpPO4Mjxkzz0+P4uPdbWbdvH0RPZ5IKxo0ZwwewGXryshSUtjSydk00uGDfayWrVdKYC7llDEoUq5nTNc6dMGM1Km+cOXEqw97Guhdr2+2Dfls5zpszLirRlr8oKtdnLYPKs6sU8vJnDpBIHjp7oMrFgzda9rN9xgBP57ILJ40axpKWB1644u+N+tYXNE20FVYN6LeBSSv85VIGof3prnntF3jx3ZWsTi2Y12Dy3r9raslG0bfd2Xb3g8J7seIyApnPh7Cs7L4HOuhAmeFWuVpjDNJztPnA0m1yQj6qt3bqPTfmXeoDmSWNZOqeBqxbNyEbWWho5a9p4v9wXhDfbFEx789zbNuzijg27O5rnjh2VNc/94+e1sKrV5rl9duJYdn9al5G1++F4nuxGjoEZi2DRi/JLoMuyyQZjJlY3bknDXkqJrXuPdIyqrc3vW9u+70jHOXOnjmdpSyMvv2ROR0PcGd7rXGgWcDXO5rmD4NjBbOZn6cjajnVwMlumhdETs5G0S17beQm0+XwY5UxcSdXV1pbYtPsgq7dkkwraR9jaOwiMCFg4fRIrFk7LZoHOaWDJ7EYaJzi5oN5YwNWYXpvnzmnkDU/Nmucun2/z3LIcfqLrLNBt92U91zqWmZqWFWgr/jAfWbs4a5Dr5AJJVXbsRBsP79jPmpLJBWu37eNQvszUmJEjOH/WZK5eMiubCTqnkUWzGhg/xi/zw4EVQJW1N8+9PW/rUdo894JZk3n1FfO4srXZ5rlnkhLs315yCTQv2J58pPOchjlZsbbkZZ0jaw1zhvMyU5JqxKFjJ1i3bX9WqG3JmuE+tP1AxzJTE8eMZHFLA69cflbetqORc2ZMcpmpYcwCboiV2zz3igXTaLL5Yc9Sgic2nTqydnBH5znTWmHO8rzHWr56wcTm6sUsSbm9h453aYa7esteNu062LHM1NQJo1k6p5HXP3U+S/NmuPObJjoRTV2UXcBFxHnAK4B5QJebgVJKN1Q4rrpxpua5z100k1XnNLFyYTOzGr2h9BQnT8Cuh05t23F0X3Z8xKhsmalzn9s5uWDWUhg7ubpxq+aYwzTUUkrs2H+0SzPc1Vv2dfTjBGhpHMfilkZedFFLx+SC2S4zpTKUVcBFxAuBbwK/Bp5C1gizFRgL/NegRVdQ2/ce4faNu7htfdfmudNLmueuXNjsdO3ujh+BHWu6jqw9vgZO5DOpRo3PirML/1vnJdDpi2C0ha96Zw7TYEsp8cieQ12a4a7ZupddB451nLOweSKXzJvCa1eczdI52WVQlylUf5U7AveXwAdTSh+JiP3AdcBW4J+A2wcruKI4XfPcxvFZ89w/eEY28aB1+iQLtnZH9uXLTJVcAt35AKTs5lzGNmZF2mW/3zmy1nSOy0ypv8xhqpgTJ9vYsPNgl2a4a7fuY39+//KoEcG5MyfzzPNndDTDXTS7gUljzV+qnHL/Np0PfC1/fhyYkFI6EhF/CXwP+NvBCK5Wna557sQxI7liYZPNc7s7sBO239v1EuiejZ3HJ83MCrTzn985sjblbCcXqJLMYeqXI8dP8uD2/V0Wb3+gZJmpcaNHsGh2A9dc0tLRDPfcmZNcZkqDrtwCbj/Qfp1qG3AOsDp//dRBiOsUEXE18ClgJPC5lNJHh+LnQjY76O7NT3T0Yuupee7KvHnu6OHcPDcl2Ptot8kF98L+bZ3nTDk7K9AufjXMWpYVbC4zpcFX1RxWzfyl8u07cryjt1r7bND1Ow9wsmSZqaUtjVy34uyO+9UWuMyUqqTcAu5O4Klki0J/D/ifEbEMeBlDcPkhIkYCnwGeCzwG3BUR30kpre39lf1zuua5o0YEl8ybwlvz5rmXDOfmuW0nYXe+zNT2kpG1w09kx2MENJ8HC57euXj7rItg/JTqxq3hqmo5bKjzl8qz68DRkpULsma4v919qOP49MljWdrSwHMXz+y4X23uVO9bVu0ot4B7FzApf/4BYDLwu8BD+bHBdjmwPqW0ESAivgpcQ5aMB6y0ee7tG3Zz1+Y9pzTPXdnaxGXDtXnuiWOwc13XS6Db74fjebIbOSZbVmrRS/JLoBfDjMUwZkJ145Y6VTOHDWr+Uu9SSmx58jCrt2RLTLWvXPD4vqMd58ybNoElLQ38t6fMZUn7MlOTnRyl2lZWNdKeePLnh4A/HLSIejYHeLRk+zHgiv6+WVtbYt32fflqBz03z13V2szlC6bROH6YNc89dhC2r+46srZjHbRlveoYMykbSbv09zr7q00/H0YOs9+TCqXKOayi+Uund7ItsWnXwY5ZoO0jbHsPdy4zdc6MSaxqbe5ohru4pWH45XnVhXLbiGwELksp7e62fwpwT0pp4WAE1xcRcSNwI8C8efO6HOu1eW7zRK65OLuHbcXCJpqHU/PcQ3u69lfbdi/sXg/k3SQnNGVF2sq3ZH/OXgZTF7jMlAqn6DlMpzp2oo2HHt/fpVhbt20/h493LjN1wezJvODCWdmaoC0NXOAyU6oj5V4PnE928213Y8m+XQ62LcBZJdtz830dUko3AzcDLF++PD2651BH89zbNnRtnvucRTOzXmytTcxuHD8E4VdZStlEgu6TC/aWDAo0zM0KtAtf0Tmy1tDiTFDVi/lUL4edMX/BqTlskGMqlINHT/DA9n1dmuE+vGM/x09mv6ZJY0exeHYD//2yszomF5wzY9LwnlSmutdrARcRLy/ZfGFE7C3ZHglcBWwehLi6uws4NyIWkCW+a4FXn+7kB7bv52kf/w8Amid1Ns9d1ToMmue2tWXLTHUfWTu0Kz8hsn5qZ12e9VjrWGaqqaphS4OhRnJYn/LXcPfEwWOds0Dz+9U27TpIykvaaRPHsKSlgaeft7BjcsHZ0ybYsknDzplG4L6R/5mAz3c7dpws8b27wjGdIqV0IiLeCvyALOl+IaW05nTnjx89kg++ZAmrWps4Z0YdN889eQJ2Pdh1csG2++BY1peOEaNgxiI47+rO/mozl8LYSb2/r1Q/qp7D+pq/houUEo/vO9qlGe6arV2XmZozZTyLWxp4ybKWbE3QOQ3ManCZKQnOUMCllEYARMQmsvtHdvV2/mBKKX0f+H45557dNIHrV80f3ICG2vHD8Pha2PabzkLt8TVwMp9JNWo8zLoQlv33zpULZiyCUcPonj6pm1rJYX3JX/WorS1fZqrkfrW1W/ex+2C2zFQELGieyKVnT+X3Vp7dcc/aVJeZkk6r3FmoCwY7EJU4srdzman2S6C7HupcZmpcY1agXf7GrGXH7Iuyy6IjvDlX6ok5bOgcP9nG+h0HujTDXbttX8dM/1EjgvNmTubZF8zouF9t0ewGJrrMlNQnZf+LiYg3A28BFgBLU0obI+JPgY0ppa8PVoB178COvFArmVzwxObO45NmZcXaohd13q82ZZ6TC6Q+ModV3pHjJ1m3bV/HElNrtu7lge37OZYvMzV+9EgWzZ7Myy6Z03G/2rkzJw3fBuhSBZXbRuSdwE3Ax4DSJWC2AG8FTH5nkhI8+UjXxdu33QsHtneeM3VBVqRdcl3nyNqkGdWLWaoT5rCB23u4fZmpznvWNuw82LHMVOP40SxpaeD6laXLTE1ipJMLpEFR7gjcm4A3ppS+FxEfKtl/D7Ck8mEVXNvJrJ9al5G1++DIk9nxGJk1v134zLy/2kXZ/WvjGqsZtVTPzGF9sGP/kc4lpvJJBo/s6VxmambDWJa0NHL1klkszu9Xc5kpaWiVW8CdTbbwc3fHgWHQSK0XJ45mKxWUXgJ9fE3JMlNjs2Wmlrw0vwS6DGYuhtHD+9cmDTFzWA9SSjz2xOFTVi7Ysb9zmamzmyawdE7WY6199YLpk50cJVVbuQXcRuBS4Lfd9r+A4bSe39ED8PjqrpMLdq6DtuzmXMY2ZCNpl17fObLWfJ7LTEnVN+xz2Mm2xMadB7oUamu27mXfkSx/jRwRnDN9Ek89t7ljFujilgYaxpm/pFpUbgH3CeDTETEBCGBlRFxHdk/JDYMVXFUd3N25Fmj7yNruDXQuM9WcFWnnPqdzckM2qlQAABIuSURBVIHLTEm1aljlsKMnTvLQ9gPZqgX56Nq6bfs4cjybXDBm1AgWzZrMi5a1dIyqXTBrMuNGO7lAKopy24h8MSJGAR8GJgD/BGwF3p5S+togxjf4UoJ9W0+dXLDvsc5zGudlo2kXvrJzZG3ybGeCSgVRzznswNET2UzQLXtZnc8Gffjx/ZzIJxdMHjuKRS0NvPrys7NibU4DrdNdZkoqurLbiKSUPgt8NiKagREppR2DF9YgaV9mattvuo6sHWpf3zqg+VyYt6Jz5YJZF8GEaVUNW9LA1UMO23PwWMdaoGu2Zs1wN+3uXGaqedIYlrQ08qzzp7OkpZGlcxo4a6rLTEn1qM+dE6u5GkOfnDwOOx/sOrK2/f6SZaZGZysVnP/8rGXHrIuyyQYuMyXVtaLksK1PHj7lfrVte490HJ8zZTxL5zTw0kvmsKSlgaVzGpkxeawzQaVhotw+cPfTcfPXqVJKF1UsokrY+SB8eE7nMlOjJ+TLTF3beQl0+iIY5TIt0nBQtBy2dts+Vn30p0B2p8bC5olcvmBaVqi1NLK4pYEpE8xf0nBW7gjcN7ptjwYuBq4EPlPRiCphxEi44sbOkbWmVpeZkoa3QuWwhnGj+ctrlrCkpZFFsyczYYzLTEnqqtxJDB/saX9E/DFZf6Xa0nQO/M6HznyepGGhaDls7tTx/N7K+dUOQ1ING+g0pG8Br6lEIJJUBeYwSYU00ALu6cChM54lSbXJHCapkMqdxPCd7ruA2cAlQI+XJiSpVpjDJNWbcu+M3d1tuw1YA7wvpfTDyoYkSRVnDpNUV8qdxPD6wQ5EkgaLOUxSvXEtFUmSpIIp9x64TfTSBLNUSmnhgCKSpAozh0mqN+XeA3cL8C7gl8Dt+b6VwOXA3wIHKh6ZJFXOLZjDJNWRcgu4BcDHUkofLt0ZEe8FlqSUXlvxyCSpcsxhkupKuffAvRz4eg/7/xV4SeXCkaRBYQ6TVFfKLeAOAs/sYf8zsQmmpNpnDpNUV8q9hPp3wGciYjlwR75vBXA98IFBiEuSKskcJqmulNsH7uMRsRl4B/DKfPc64PqUUk+XJSSpZpjDJNWbckfgyJOciU5SIZnDJNUTG/lKkiQVzGlH4CJiH7AwpbQrIvbTSxPMlFLDYAQnSf1lDpNUz3q7hPo2YH/J87K6mEtSjTCHSapbpy3gUkpfKnl+y5BEI0kVYg6TVM/KugcuIjZGRFMP+6dExMbKhyVJlWMOk1Rvyp3EMB8Y2cP+scDcikUjSYNjPuYwSXWk1zYiEfHyks0XRsTeku2RwFXApsEITJIGyhwmqV6dqQ/cN/I/E/D5bseOA5uBd1c4JkmqFHOYpLrUawGXUhoBEBGbgMtSSruGJCpJqgBzmKR6Ve5SWgsGOxBJGizmMEn1ptdJDBFxW0RMKdn+SERMK9lujohHBjNASeovc5ikenWmWagrgDEl228BppRsjwTmVDooSaoQc5ikutTXtVBjUKKQpKFhDpNUF1zMXpIkqWDOVMAlTl0/0PUEJRWFOUxSXTrTLNQAvhwRR/PtccBnI+JQvj120CKTpIEzh0mqS2cq4L7UbfvLPZxza4VikaRKM4dJqktnauT7+qEKRJIqzRwmqV45iUGSJKlgLOAkSZIKxgJOkiSpYCzgJEmSCsYCTpIkqWAs4CRJkgrGAk6SJKlgLOAkSZIKxgJOkiSpYCzgJEmSCsYCTpIkqWBqooCLiL+JiAci4r6I+LeImFJy7L0RsT4iHoyI51UzTknqiTlM0lCriQIO+BGwNKV0EfAQ8F6AiFgMXAssAa4G/iEiRlYtSknqmTlM0pCqiQIupfTDlNKJfPMOYG7+/BrgqymloymlTcB64PJqxChJp2MOkzTUaqKA6+YG4P/kz+cAj5YceyzfJ0m1yhwmadCNGqofFBE/Bmb1cOjPUkrfzs/5M+AE8JV+vP+NwI0A8+bNG0CkknQqc5ikWjJkBVxK6Tm9HY+I1wEvAq5KKaV89xbgrJLT5ub7enr/m4GbAZYvX556OkeS+sscJqmW1MQl1Ii4GrgJeElK6VDJoe8A10bE2IhYAJwL/LIaMUrS6ZjDJA21IRuBO4NPA2OBH0UEwB0ppTellNZExNeBtWSXJd6SUjpZxTglqSfmMElDqiYKuJTSOb0c+2vgr4cwHEnqE3OYpKFWE5dQJUmSVD4LOEmSpIKxgJMkSSoYCzhJkqSCsYCTJEkqGAs4SZKkgrGAkyRJKhgLOEmSpIKxgJMkSSoYCzhJkqSCsYCTJEkqGAs4SZKkgrGAkyRJKhgLOEmSpIKxgJMkSSoYCzhJkqSCsYCTJEkqGAs4SZKkgrGAkyRJKhgLOEmSpIKxgJMkSSoYCzhJkqSCsYCTJEkqGAs4SZKkgrGAkyRJKhgLOEmSpIKxgJMkSSoYCzhJkqSCsYCTJEkqGAs4SZKkgrGAkyRJKhgLOEmSpIKxgJMkSSoYCzhJkqSCsYCTJEkqGAs4SZKkgrGAkyRJKhgLOEmSpIKxgJMkSSoYCzhJkqSCsYCTJEkqGAs4SZKkgrGAkyRJKhgLOEmSpIKxgJMkSSoYCzhJkqSCsYCTJEkqGAs4SZKkgrGAkyRJKhgLOEmSpIKxgJMkSSoYCzhJkqSCsYCTJEkqGAs4SZKkgrGAkyRJKhgLOEmSpIKpqQIuIt4dESkimvPtiIi/j4j1EXFfRFxa7Rgl6XTMYZKGSs0UcBFxFvA7wCMlu58PnJs/bgT+sQqhSdIZmcMkDaWaKeCAvwNuAlLJvmuAW1PmDmBKRMyuSnSS1DtzmKQhUxMFXERcA2xJKd3b7dAc4NGS7cfyfZJUM8xhkobaqKH6QRHxY2BWD4f+DHgf2aWHgbz/jWSXKJg3b95A3kqSTmEOk1RLhqyASyk9p6f9EXEhsAC4NyIA5gL3RMTlwBbgrJLT5+b7enr/m4GbAZYvX556OkeS+sscJqmWVP0Sakrp/pTSjJTS/JTSfLJLDJemlLYD3wF+L5/JtQLYm1LaVs14JamUOUxSNQzZCFw/fR94AbAeOAS8vrrhSFKfmMMkDYqaK+Dyb7DtzxPwlupFI0l9Yw6TNBSqfglVkiRJfWMBJ0mSVDAWcJIkSQVjASdJklQwFnCSJEkFYwEnSZJUMBZwkiRJBWMBJ0mSVDAWcJIkSQVjASdJklQwFnCSJEkFYwEnSZJUMBZwkiRJBWMBJ0mSVDAWcJIkSQVjASdJklQwFnCSJEkFYwEnSZJUMBZwkiRJBWMBJ0mSVDAWcJIkSQVjASdJklQwFnCSJEkFYwEnSZJUMBZwkiRJBWMBJ0mSVDAWcJIkSQVjASdJklQwkVKqdgwVFxH7gQerHUeFNAO7qh1EhdTLZ6mXzwH19VnOTylNrnYQlVBHOaye/n75WWpPvXwO6Ef+GjVYkVTZgyml5dUOohIi4m4/S22pl88B9fdZqh1DBdVFDqu3v19+ltpSL58D+pe/vIQqSZJUMBZwkiRJBVOvBdzN1Q6ggvwstadePgf4WWpVvXyWevkc4GepRfXyOaAfn6UuJzFIkiTVs3odgZMkSapbdVfARcTVEfFgRKyPiD+tdjz9ERFnRcR/RMTaiFgTEe+odkwDFREjI+LXEfHdascyEBExJSK+EREPRMS6iFhZ7Zj6KyL+KP/7tToi/iUixlU7pnJFxBciYkdErC7ZNy0ifhQRD+d/Tq1mjP1RD/kL6i+Hmb9qj/mrzgq4iBgJfAZ4PrAYeFVELK5uVP1yAnh3SmkxsAJ4S0E/R6l3AOuqHUQFfAr495TSBcAyCvqZImIO8HZgeUppKTASuLa6UfXJLcDV3fb9KfCTlNK5wE/y7cKoo/wF9ZfDzF81xPyVqasCDrgcWJ9S2phSOgZ8FbimyjH1WUppW0rpnvz5frJ/ZHOqG1X/RcRc4IXA56ody0BERCPwdODzACmlYymlJ6sb1YCMAsZHxChgArC1yvGULaX0c2BPt93XAF/Kn38JeOmQBjVwdZG/oL5ymPmrZg37/FVvBdwc4NGS7ccoaNJoFxHzgUuAO6sbyYB8ErgJaKt2IAO0ANgJfDG/nPK5iJhY7aD6I6W0BfgE8AiwDdibUvphdaMasJkppW358+3AzGoG0w91l7+gLnKY+avGmL8y9VbA1ZWImAR8E3hnSmlftePpj4h4EbAjpfSrasdSAaOAS4F/TCldAhykYJfp2uX3V1xDltRbgIkR8drqRlU5KZte7xT7Kit6DjN/1SbzV6beCrgtwFkl23PzfYUTEaPJEt9XUkrfqnY8A3Al8JKI2Ex2SejZEfHl6obUb48Bj6WU2kcSvkGWEIvoOcCmlNLOlNJx4FvAqirHNFCPR8RsgPzPHVWOp6/qJn9B3eQw81dtMn9RfwXcXcC5EbEgIsaQ3dT4nSrH1GcREWT3KaxLKf1tteMZiJTSe1NKc1NK88n+e/w0pVTIb0oppe3AoxFxfr7rKmBtFUMaiEeAFRExIf/7dhUFvaG5xHeA6/Pn1wPfrmIs/VEX+QvqJ4eZv2qW+Ys6W8w+pXQiIt4K/IBsVsoXUkprqhxWf1wJXAfcHxG/yfe9L6X0/SrGpMzbgK/k/4PdCLy+yvH0S0rpzoj4BnAP2YzBX1OgruYR8S/AM4HmiHgMeD/wUeDrEfEG4LfAK6sXYd/VUf4Cc1itMn/VgErlL1dikCRJKph6u4QqSZJU9yzgJEmSCsYCTpIkqWAs4CRJkgrGAk6SJKlgLOBUERHxuog4UO04BltEfCAiVlc7DkmVZQ5T0VjAqYuIuCUiUg+PO0rOSRHxim4v/RqwcGij7TWe/rzPM0/z2VNEXJCf9gngGQP9WZIGhznMHDZc1FUjX1XMj8macJY61tsLUkqHgcODFtHQWgLs6bZvJ0BK6QBQ99/SpYIzh5nD6p4jcOrJ0ZTS9m6PPQD5moAA/5p/q9uc7z/l8kNE3BQR2yPiQETcmg/dby45fktEfLfba04Z3o+I10fE2og4EhEPRcQfRcSIM8TTGhHfzn/+wYi4J1+Yuhw7evj8J/sTX3eR+VFE/DhfAoaImBQRD0fEZ8qMT1LvzGHmsLpnAae+uiz/843A7JLtLiLilcCHyJYIuRR4EHhXX39YRLwR+DDwF8Ai4N3AnwBvPkM8k4D/AzwXWEa2qPa3Si4jVEQZ8XWRsqVPrgcuBt6T7/57stGB9/T0GkkVZQ7rW3xdmMNqSErJh4+OB3AL2dpyB7o9PlZyTgJe0e11rwMOlGzfBny22zk/BjZ3+1nf7XbOB4DVJduPANd1O+edwNre4jnNZ7sD+B+9HH9m/l7dP/tjA4nvND/rpcBR4K/yP5dV+7+9Dx/18DCHmcOGy8N74NSTnwM3dtv3ZB/fYxHwuW77bgfOKfcNImI6cBbwvyPiH0sOjQLiDK+dSPbN+UVk32pHA+OA+8r40c8CnijZPlnp+FJK/29E/DPwP4CbUkr3lhGXpPKYw8xhdc8CTj05lFJaPwQ/p41Tk8Tokuftl/jfRPZtuC8+AVxNNqT/MHAIuBUYU8ZrN6WUdpVxXr/ji4hxZJdKTtKH/yFIKos5zBxW9yzg1B/HgZFnOGcdsAL4Qsm+Fd3O2Ul2H0Wpju2U0uMRsRVoTSnd2sd4ngrcmlL6JnQkm1bgoTPEXbY+xNeTvwHGkt3f8oOI+F5K6TuVik1Sr8xhfYuvJ+awKrOAU0/GRsSsbvtOppR25s83A1dFxH+SzfZ6glN9Crg1Iu4Cfga8AriCrlPbfwrcFBE3kF3yeDlwJfBYyTnvB/5XRDwJfJ/s2+2lwJyU0kd6iech4GUR8W2y5Ph+sssP5ZgREd3/bexJKfXUhqCc+LqIiOcDfwA8LaV0Z0R8APhcRFyUUtpeZoySTs8cZg6rf9W+Cc9HbT3IbspNPTxKb4J9MdmQ/nHyG3rpdgNwvu+9wA6ym2j/mezm2c3dzvkAsA3YC/wD2Wyo1d3OeRVwD3CE7L6OXwDXniGes8luOD5IlkzfA3wXuKWXz/7M03z2BDynJN4+xdft3OnAduAvSvaNAP6TbMZZVPvvgA8fRX6Yw8xhw+UR+S9fGnQR8R7grSml+dWOpb8i4iPAs1JK3S+lSKpz5jDVEi+hSmXIG1YuBK6ivFlgklQzzGH1x0a+UnkagbVkzSr/qsqxSFJfmcPqjJdQJUmSCsYROEmSpIKxgJMkSSoYCzhJkqSCsYCTJEkqGAs4SZKkgrGAkyRJKpj/H299IsIYQ5HJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##5.Biblioteca pandas\n",
        "\n",
        "El enlace:\n",
        "https://pandas.pydata.org/\n",
        "\n",
        "Se presenta un ejemplo:"
      ],
      "metadata": {
        "id": "LW6VXO1EUBKm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "url = \"https://datosabiertos.metropol.gov.co/sites/default/files/EnfermedadesCalculadas_2008-2017.csv\"\n",
        "df = pd.read_csv(url,sep=';')\n",
        "df.describe()\n",
        "df.tail()\n",
        "df.head()\n",
        "by_municipio = df.groupby(['Municipio','Sexo'])['casos'].sum()"
      ],
      "metadata": {
        "id": "bzSrGZU2UPSd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##6.Open IA\n",
        "\n",
        "Enlace: https://openai.com/\n",
        "\n",
        "Ejemplo:"
      ],
      "metadata": {
        "id": "LdUUPuAfUad9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install openai"
      ],
      "metadata": {
        "id": "T-b_DXoDUqyd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "openai.api_key = \"\"\n",
        "\n",
        "response = openai.Completion.create(\n",
        "  engine=\"text-davinci-002\",\n",
        "  prompt=\"Create a list of 8 questions for my interview with a science fiction author:\",\n",
        "  temperature=0.5,\n",
        "  max_tokens=150,\n",
        "  top_p=1.0,\n",
        "  frequency_penalty=0.0,\n",
        "  presence_penalty=0.0\n",
        ")"
      ],
      "metadata": {
        "id": "mH07Sv5SUral"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##¿Dónde encontrar más información?\n",
        "\n",
        "- [Lenguaje Markdown incluyendo ecuaciones](https://colab.research.google.com/notebooks/markdown_guide.ipynb#scrollTo=70pYkR9LiOV0)\n",
        "- [Introducción a Python](https://colab.research.google.com/github/FerneyOAmaya/DataLiteracy/blob/master/0_Python.ipynb)\n",
        "- [Simulación movimiento parabólico](https://colab.research.google.com/github/FerneyOAmaya/DataLiteracy/blob/master/Parabolico.ipynb)"
      ],
      "metadata": {
        "id": "r1tt8Nh1NjWG"
      }
    }
  ]
}