Python-Clase-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python para Psicólogos\n",
    "## Javier Villanueva-Valle\n",
    "<javier830409@gmail.com>\n",
    "\n",
    "* Guido Van Rossum en 1991\n",
    "* Propósito General: Construir lo que gustes y mandes\n",
    "* ¡Open Source! ¡Gratis!\n",
    "* Paquetes Python, también para ciencia de datos\n",
    "* Muchas aplicaciones y muchos campos\n",
    "    * Desarrollo web e Internet.\n",
    "    * Acceso a la base de datos.\n",
    "    * GUIs de escritorio.\n",
    "    * Científico y numérico.\n",
    "    * Educación.\n",
    "    * Programación de red.\n",
    "    * Desarrollo de Software y Juegos.\n",
    "* [**Python**](https://www.python.org/) Version 3.x \n",
    "* [Anaconda](https://www.anaconda.com/products/individual)\n",
    "\n",
    "### Ventajas\n",
    "* Simplificado y rápido\n",
    "* Elegante y flexible\n",
    "* Programación sana y productiva\n",
    "* Ordenado y limpio\n",
    "* Lenguaje Interpretado\n",
    "* Multiplataforma\n",
    "* Extensa comunidad\n",
    "\n",
    "### Desventajas\n",
    "* Curva de aprendizaje\n",
    "* Librerías propias que están incluidas (Usan librerías de terceros)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ¿Por qué usar Jupyter?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Jupyter](https://github.com/Sivlemx/Intro-Python-DataScience/raw/master/Jupyter.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inicio 2 Julio 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Comencemos](https://i.imgur.com/5A22MNp.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modulo I: Uso de Anaconda - Instalación\n",
    "* [Documentación en línea](https://docs.anaconda.com/anaconda/user-guide/getting-started/)\n",
    "* [Uso del Navegador](https://docs.anaconda.com/anaconda/navigator/getting-started/)\n",
    "* [Guía Inicial](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html)\n",
    "* [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/)\n",
    "* [Uso de Conda (*crear ambiente)*](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html)\n",
    "* [Trucos en Conda](https://conda.io/projects/conda/en/latest/_downloads/843d9e0198f2a193a3484886fa28163c/conda-cheatsheet.pdf)\n",
    "* [Documentación Conda](https://conda.io/projects/conda/en/latest/user-guide/index.html)\n",
    "\n",
    "~~~\n",
    "conda info --envs\n",
    "conda create --name \"el-nombre-que-yo-quiera\"\n",
    "conda info --envs\n",
    "conda activate \"el-nombre-que-yo-quiera\"\n",
    "conda update conda\n",
    "conda update anaconda\n",
    "conda update --all -y\n",
    "conda list\n",
    "conda install pandas numpy matplotlib spyder\n",
    "conda install -c conda-forge jupyterlab\n",
    "conda install -c conda-forge notebook\n",
    "conda list\n",
    "~~~"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modulo II: Tipos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- (str) -> string -> 'Python para Psicólogos', '2020', '0.05'\n",
    "- (int) -> integer -> 2020, 2, 0, 20\n",
    "- (float) -> float -> 1.5, 0.05\n",
    "- (bool) -> bool -> True, False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ejemplos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nombre = 'Javier'\n",
    "edad_str = '37'\n",
    "edad_int = 37\n",
    "edad_float = 37.0\n",
    "bool1 = True\n",
    "bool2 = False\n",
    "sexo_bool = '10'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(type(nombre))\n",
    "print(type(edad_str))\n",
    "print(type(edad_int))\n",
    "print(type(edad_float))\n",
    "print(type(bool1))\n",
    "print(type(bool2))\n",
    "print(sexo_bool.isalpha())\n",
    "print(sexo_bool.isdigit())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modulo III: Variables\n",
    "#### Operadores aritméticos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "val1 = 'Javier'\n",
    "val2 = '2'\n",
    "val3 = 'Quiero juntar las dos variables, ¿Cómo?' # Pista '+'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "val1 = val1 + ' ' + val2 + ',' + ' '\n",
    "val1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "suma = 4 + 7\n",
    "resta = 4 - 7\n",
    "multi = 4 * 7\n",
    "exponente = 4 ** 7\n",
    "div = 13 / 7\n",
    "div_int = 13 // 7\n",
    "residuo = 13 % 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "suma = 'Javier' + 3\n",
    "resta = 'Javier' - 3\n",
    "multi = 'Javier' * 3\n",
    "exponente = 'Javier' ** 3\n",
    "div = 'Javier' / 3\n",
    "div_int = 'Javier' // 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "oper = val1 * 3\n",
    "oper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conversión de datos <-> int-float, int-str, float-int, float-str, str-int, str-float \n",
    "suma = 4 + 7\n",
    "resta = 4 - 7\n",
    "multi = 4 * 7\n",
    "exponente = 4 ** 7\n",
    "div = 13 / 7\n",
    "div_int = 13 // 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(suma)\n",
    "print(type(suma))\n",
    "print(str(suma))\n",
    "letras = str(suma)\n",
    "print(type(letras))\n",
    "decimal = float(suma)\n",
    "print(decimal)\n",
    "print(type(decimal))\n",
    "print(div)\n",
    "print(type(div))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(True)\n",
    "print(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bool-int\n",
    "print(int(False))\n",
    "print(int(True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Operador \"%\" para obtener el residuo de una operación\n",
    "residuo = 13 % 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(13 / 7)\n",
    "residuo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ¿Cuál es la diferencia?\n",
    "uno = 4 **  2/12\n",
    "dos = 4 ** (2/12)\n",
    "print(uno)\n",
    "print(dos)\n",
    "# ¿Por qué?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "paso1 = 2 / 12\n",
    "paso2 = 4 ** paso1\n",
    "paso2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# De acuerdo al número de posición del elemento después del \"=\", es la variable declarada.\n",
    "# Se puede aplicar cualquier operación aritmética si sé conoce la posición del elemento\n",
    "a, b, c, d = 20, 11.3, 5, 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a == d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b, c, d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Operadores de relación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(4 == 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(4 != 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(4 < 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(7 < 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(4 > 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(4 > 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(4 <= 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(4 <= 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(6 <= 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(4 >= 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(4 >= 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Javier = 6\n",
    "print(Javier)\n",
    "print(type(Javier))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(Javier == 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(type('Javier'))\n",
    "print(type(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('J', 'a', 'v', 'i', 'e', 'r' == 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len('erghñdsfgjnñgdfsknñjsfnkjsjknskjnsghñlnñflgkhñlhgks')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len('Javier') == 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('J', 'a', 'v', 'i', 'e', 'r' == 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(['J', 'a', 'v', 'i', 'e', 'r']) == 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### String"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# El símbolo \"#\" se usa para comentar dentro de celdas para códigos\n",
    "# Uso de (' '), (\" \"), (\\'), (\\\"), (\\n), (\\t)\n",
    "print('Hola')\n",
    "print(\"Hola\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Yas'tas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('\n",
    "      Hola')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\"\"\n",
    "         Hola, espero que estes re-bien.\n",
    "         Ya comiste?\"\"\")\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\"\"\n",
    "añslkdjfañsljflñkdsa,\n",
    "asdñlkfakñdjflñkadsf\n",
    "jasñdkfjñalfghñsdfngñsdf\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('He\\'s Javier')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('He\\'s Javier')\n",
    "      \n",
    "print(\"He's Javier\")\n",
    "print(\"He\\'s Javier\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\"\"Sorpresa\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Javier\\nVillanueva')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Javier\\tVillanueva')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Módulo IV: Contenedores\n",
    "#### La importancia de aprender sobre los contenedores reside en que el análisis de datos existen diferentes tipos de datos. Aprender a manejar listas equivale a manipular, estructurar, analizar y gráficar datos (los que sean).\n",
    "#### Listas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = ['Javier', 'Villanueva', 2020, True, 6.7, 'Javier', 'Valle', 'Flora', 'Gabriel', 'Mayra', 'Olivia', 9, '9']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(lista)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_recorda = lista[1:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(lista_recorda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_alreves = lista[:-4]\n",
    "print(lista_alreves)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista[:-4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = ['Javier', 'Villanueva', 2020, True, 6.7, 'Javier', 'Valle', 'Flora', 'Gabriel', 'Mayra', 'Olivia', 9, '9']\n",
    "\n",
    "for posición, contenido in enumerate(lista):\n",
    "    print('El lugar {} contiene {} es de tipo {}'.format(posición, contenido, type(contenido)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista[0] * lista[11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for posición, contenido in enumerate(lista, 1):\n",
    "    print('En el {} está {}'.format(posición, contenido))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'ñsdjflñaksdjflñaskfj'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'Hay {} tomando la clase de python {}'.format(60, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# capitalize, count, find, isalnum, isalpha, isdigit, islower, isupper, join, split, len, lower, upper, strip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "el_prof = 'Javier Villanueva sacó 6.7 en la Prepa'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.capitalize())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.count('a'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.find('a'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.find('a', 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if all characters in the string are alphanumeric\n",
    "es_o_no_alphanumerico = 'PythonCurso2020'\n",
    "print(es_o_no_alphanumerico.isalnum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if all characters in the string are alphabets (can be both lowercase and uppercase)\n",
    "es_o_no_alpha = 'Javier'\n",
    "print(es_o_no_alpha.isalpha())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "es_o_no_digito = '2020'\n",
    "print(es_o_no_digito.isdigit())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.islower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.isupper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "el_prof = 'Javier Villanueva sacó 6.7 en la Prepa'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.join())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('_'.join(el_prof))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original\n",
    "print(el_prof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(list(el_prof))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.split(' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "el_prof_lista = el_prof.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof_lista)\n",
    "print(type(el_prof_lista))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('_'.join(el_prof_lista))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "el_prof = 'Javier Villanueva, sacó 6.7 en la Prepa'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.split('.'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.split('a'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(el_prof))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.upper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "el_prof_sin_espacio_a_la_derecha_y_a_la_izquierda = 'Javier Villanueva tiene 5'\n",
    "print(el_prof_sin_espacio_a_la_derecha_y_a_la_izquierda)\n",
    "print(len(el_prof_sin_espacio_a_la_derecha_y_a_la_izquierda))\n",
    "\n",
    "el_prof_espaciado = '     Javier Villanueva tiene 5      '\n",
    "print(el_prof_espaciado)\n",
    "print(len(el_prof_espaciado))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(el_prof_espaciado.strip())\n",
    "print(len(el_prof_espaciado.strip()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Contar elementos (len())\n",
    "# Seleccionar elementos de la lista (Desdel el inicio y desde el Final)\n",
    "# len() aninado lista[-len(lista)]\n",
    "\n",
    "# Agregar nuevo elemento a la lista (.append())\n",
    "\n",
    "# Sustituir elementos lista[0] = 'Javi'\n",
    "\n",
    "# Conteo de un mismo elemento dentro de la lista (.count(\"elemento\"))\n",
    "\n",
    "# Extender la lista (.extend(range(1, 5)); .extend(4); .extend(\"siempre debe de ser un iterador\"))\n",
    "\n",
    "# Saber el lugar del elemento dentro de la lista (.index(); .index('Javier', 1) a partir de ese lugar buscar)\n",
    "\n",
    "# Insertar elementos en el lugar que queremos (.insert(\"lugar del elemento a insertar\", \"Elemento a insertar\"))\n",
    "\n",
    "# Listas dentro de otras listas (lista.append(['Valle', 665.999]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nombre = 'Python para Psicólogos y los no programadores'\n",
    "dir_trabajo_windows = '\\Javier\\Document\\Final\\Final\\Trabajo ultimisimo.docx' \n",
    "dir_trabajo_unix = '/Javier/Document/Final/Final/Trabajo ultimisimo.docx'\n",
    "\n",
    "# Separar string (cadenas) para generar listas (.split())\n",
    "nombre.split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_trabajo_windows.split('\\\\''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nombre = ['Javier Villanueva Valle']\n",
    "#print(nombre.split(' '))\n",
    "type(nombre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nombre = str(nombre)\n",
    "#type(nombre)\n",
    "nombre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nombre = nombre.strip('[]')\n",
    "nombre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nombre = nombre.split(' ')\n",
    "nombre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "primer_nombre = nombre[0]\n",
    "primer_nombre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "primer_nombre = primer_nombre.strip(\"''\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "primer_nombre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(primer_nombre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "primer_nombre = primer_nombre.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "primer_nombre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "primer_nombre = primer_nombre + ' ' + 'Villanueva'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "primer_nombre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Función \"range\"\n",
    "##### También es un iterador. Un iterador se refiere al objeto que permite al programador recorrer un contenedor, particularmente listas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hartos_numeros = range(-100, 100)\n",
    "print(type(hartos_numeros))\n",
    "print(hartos_numeros)\n",
    "print(list(hartos_numeros))\n",
    "print(len(list(hartos_numeros)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "otros_num = list(range(0, 10))\n",
    "print(otros_num)\n",
    "otros_num.append(2020) # se adhiere un elemento a la vez\n",
    "print(otros_num)\n",
    "otros_num.append(2)\n",
    "print(otros_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# .count() cuenta el número de elementos dentro de un contenedor\n",
    "otros_num.count(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# .extend() SIEMPRE DEBE SER UN ITERADOR\n",
    "otros_num.extend(range(1, 5))\n",
    "otros_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "otros_num.extend(4)\n",
    "otros_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# .index() conocer la posición del elemento deseado\n",
    "otros_num.index(2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "otros_num.index(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "otros_num.index(2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(otros_num)\n",
    "for elemento in otros_num:\n",
    "    #print(elemento)\n",
    "    print('El elemento {} se encuentra en la posición {}'.format(elemento, otros_num.index(elemento)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for lugar, elemento in enumerate(otros_num):\n",
    "    print(lugar, elemento)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "busqueda = []\n",
    "for lugar, elemento in enumerate(otros_num):\n",
    "    #busqueda.append((elemento, otros_num.index(elemento, lugar)))\n",
    "    busqueda.append('El {} se encuentra en el lugar {}'.format(elemento, otros_num.index(elemento, lugar)))\n",
    "\n",
    "print(busqueda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cosa in busqueda:\n",
    "    print(cosa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### set()\n",
    "###### Representa conjuntos. Un conjunto es una colección desordenada de elementos únicos, es decir, que no se repiten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = ['c', 'u', 'r', 's', 'o', ' ', 'd', 'e', ' ', 'p', 'y', 't', 'h', 'o', 'n', ' ',\n",
    "       'f', 'á', 'c', 'i', 'l', ' ', 'y', ' ', 'p', 'r', 'á', 'c', 't', 'i', 'c', 'o']\n",
    "print(set(obj))\n",
    "print(''.join(obj))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### union, intersection, difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Union](https://media.geeksforgeeks.org/wp-content/cdn-uploads/Union-in-python.jpg)\n",
    "![Intersection](https://media.geeksforgeeks.org/wp-content/cdn-uploads/intersection-in-python.jpg)\n",
    "![Difference](https://media.geeksforgeeks.org/wp-content/cdn-uploads/set-difference.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = set('curso')\n",
    "b = set('python')\n",
    "\n",
    "print(a.union(b))\n",
    "print(a.intersection(b))\n",
    "print(a.difference(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uno = eval('[2, 4, 6, 8]')\n",
    "\n",
    "dos = eval('max([1, 3, 5, 7, 9])')\n",
    "\n",
    "tres = eval('min([1, 9, 5, 0, -3])')\n",
    "\n",
    "cuatro = eval('20 * 2')\n",
    "\n",
    "otro_cuatro = '20 * 2'\n",
    "\n",
    "print(uno)\n",
    "print(type(uno))\n",
    "print(dos)\n",
    "print(type(dos))\n",
    "print(tres)\n",
    "print(cuatro)\n",
    "print(otro_cuatro)\n",
    "print(type(otro_cuatro))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ¿Qué es la función \"lambda\"?\n",
    "![lambda](https://media1.tenor.com/images/76dceaeec08bbc48debd5a28e35d5839/tenor.gif?itemid=12147563)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Esa \"lambda\" tan servicial y anónima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "doble = lambda x: x * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "doble(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(doble(2020))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = lambda a: a + 10\n",
    "print(x(-2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = lambda a, b : a * b\n",
    "print(x(-3, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = lambda a, b, c : a + b + c\n",
    "print(x(20, 21, 22))\n",
    "#print(x(20, 21))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### sorted()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordenado = sorted([9, 1, 8, 2, 7, 3, 6, 4, 5, -2, -100, 1000])\n",
    "print(ordenado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "al_reves = sorted([9, 1, 8, 2, 7, 3, 6, 4, 5, -2, -100, 1000], reverse=True)\n",
    "print(al_reves)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Introduciendo cosas nuevas\n",
    "## lambda: función anónima\n",
    "## len: conteo de elementos\n",
    "palabras = ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría']\n",
    "\n",
    "print(palabras)\n",
    "print(sorted(palabras))\n",
    "print(len(palabras))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "orden = sorted(palabras, key=lambda x:len(x))\n",
    "print(orden)\n",
    "orden = sorted(orden, reverse=True)\n",
    "print(orden)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### reversed()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uno = reversed('abcdehijbchijklmnño')\n",
    "print(uno)\n",
    "print(list(uno))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uno = list(reversed('abcdehijbchijklñajgflñfskfjñgfldksjfgñldksñlfmñsldfgjmnño'))\n",
    "print(uno)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dos = reversed([2, 4, 6, 8, 10, 12, 14, 16])\n",
    "print(dos)\n",
    "print(list(dos))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[pandas - Python Data Analysis Library](https://pandas.pydata.org/)\n",
    "![pandas](https://upload.wikimedia.org/wikipedia/commons/thumb/e/ed/Pandas_logo.svg/245px-Pandas_logo.svg.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Vistazo rápido a Pandas No. 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lista natural\n",
    "lista_1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
    "print(lista_1)\n",
    "\n",
    "# Lista \"range\"\n",
    "lista_2 = list(range(10, 20))\n",
    "print(lista_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataFrame\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = pd.Series(lista_2)\n",
    "print(ds)\n",
    "print(type(ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = pd.Series(lista_2, name='Tiempo')\n",
    "print(ds)\n",
    "print(type(ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([lista_1, lista_2])\n",
    "print(df)\n",
    "print(type(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Columna1'] = lista_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Columna2'] = lista_2\n",
    "print(df)\n",
    "print(type(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### FIN del Vistazo rápido a Pandas No. 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sustituión de \"def\"\n",
    "#### La sentencia def es una definición de función usada para crear objetos funciones definidas por el usuario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "palabras = ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría']\n",
    "\n",
    "def mayuscula(palabra):\n",
    "    return palabra.lower()\n",
    "\n",
    "print(mayuscula('Javier'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mayus = mayuscula('Curso Python')\n",
    "print(mayus)\n",
    "otra_cosa = mayuscula('cANción')\n",
    "print(otra_cosa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### map()\n",
    "##### se utiliza mucho junto a expresiones lambda ya que permite ahorrarnos el esfuerzo de crear bucles for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "palabras = ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría']\n",
    "print('Esto contiene la lista que queremos que sean en mayusculas = {}'.format(palabras));print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mayusculas = []\n",
    "print('Lo que contiene \"mayusculas\" {} ANTES del ciclo \"for\"'.format(mayusculas))\n",
    "\n",
    "for palabra in palabras:\n",
    "    print(palabra.upper())\n",
    "    mayusculas.append(palabra.upper())\n",
    "\n",
    "print(' ')\n",
    "print('Lo que contiene \"mayusculas\" {} DESPUÉS del ciclo \"for\"'.format(mayusculas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(mayusculas))\n",
    "print(mayusculas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "palabras = ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría']\n",
    "mayusculas = map(lambda x:x.upper(), palabras)\n",
    "print(mayusculas)\n",
    "print(list(mayusculas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "muchos_nums = list(range(0, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "doble = []\n",
    "for nums in muchos_nums:\n",
    "    #print(nums, end=',')\n",
    "    doble.append(nums * 2)\n",
    "\n",
    "print(' ')\n",
    "print(doble)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for nums in muchos_nums:\n",
    "    nums * 2\n",
    "    print(nums * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "muchos_nums = list(range(0, 10))\n",
    "\n",
    "doble = list(map(lambda x: x * 2, muchos_nums))\n",
    "print(doble)\n",
    "#print(list(doble))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nums = [2, 4, 6, 8]\n",
    "expo = map(lambda x: x * x, nums)\n",
    "print(expo)\n",
    "print(list(expo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### filter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nums = list(range(21))\n",
    "print(nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtrado_nones = filter(lambda x: x % 2 != 0, nums)\n",
    "print(filtrado_nones)\n",
    "print(list(filtrado_nones))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nums = list(range(21))\n",
    "filtrado_pares = filter(lambda x: x % 2 == 0, nums)\n",
    "print(filtrado_pares)\n",
    "print(list(filtrado_pares))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(3 / 2)\n",
    "print(3 % 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pasión']\n",
      "['ÓSCULO']\n"
     ]
    }
   ],
   "source": [
    "palabras = ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría', 'canción', 'Lluvia', 'Trueno', 'Nuboso', 'FRANCO', 'PERPLEJO', 'ÓSCULO']\n",
    "letra_filtrada_minuscula = filter(lambda x:('pasión' or 'pasion' or 'enojó') in x, palabras)\n",
    "letra_filtrada_mayuscula = filter(lambda x:'Ó' in x, palabras)\n",
    "print(list(letra_filtrada_minuscula))\n",
    "print(list(letra_filtrada_mayuscula))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[pandas - Python Data Analysis Library](https://pandas.pydata.org/)\n",
    "![pandas](https://upload.wikimedia.org/wikipedia/commons/thumb/e/ed/Pandas_logo.svg/245px-Pandas_logo.svg.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Vistazo rápido a Pandas No. 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "palabras = ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría', 'canción', 'Lluvia', 'Trueno', 'Nuboso', 'FRANCO', 'PERPLEJO', 'ÓSCULO']\n",
    "\n",
    "df = pd.DataFrame()\n",
    "df['Palabras'] = palabras\n",
    "df['Num_Letras'] = df[['Palabras']].applymap(lambda x: len(x)) # doble corchete recto\n",
    "df = df.sort_values('Num_Letras').sort_index()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## HACER UN DATAFRAME USANDO EL CICLO FOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "\n",
    "list_df = []\n",
    "\n",
    "palabras = ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría', 'canción', 'Lluvia', 'Trueno', 'Nuboso', 'FRANCO', 'PERPLEJO', 'ÓSCULO']\n",
    "\n",
    "for i in palabras:\n",
    "    list_df.append(len(i))\n",
    "    \n",
    "df['Otras_Palabras'] = palabras\n",
    "df['Letras'] = list_df\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ejem1 = df.iloc[3, 0]\n",
    "print(ejem1)\n",
    "\n",
    "ejem2 = df.loc[3, 'Otras_Palabras']\n",
    "print(ejem2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### FIN del Vistazo rápido a Pandas No. 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### enumerate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "palabras = ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría']\n",
    "for i, j in enumerate(palabras):\n",
    "    print(i, j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = list(enumerate(palabras))\n",
    "print(lista)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(lista)\n",
    "print(type(lista))\n",
    "print(type(lista[0]))\n",
    "print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordenados_por_letra_inicial = sorted(lista, key=lambda x:x[1])\n",
    "print(ordenados_por_letra_inicial)\n",
    "print(type(ordenados_por_letra_inicial))\n",
    "print(type(ordenados_por_letra_inicial[0]))\n",
    "print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordenados_por_cantidad_de_letras = sorted(lista, key=lambda x:len(x[1]))\n",
    "print(ordenados_por_cantidad_de_letras)\n",
    "print(type(ordenados_por_cantidad_de_letras))\n",
    "print(type(ordenados_por_cantidad_de_letras[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tuplas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Las tuplas son objetos de tipo secuencia, específicamente es un tipo de dato lista inmutable. \n",
    "# Esta no puede modificarse de ningún modo después de su creación.\n",
    "\n",
    "software = ('Python', 30, 'C++', True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('El elemento \"{}\" se repite {} vez'.format(software[0], software.count(software[0])))\n",
    "\n",
    "print('El elemento \"{}\" está en el lugar {}'.format(software[0], software.index(software[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lugar = 3\n",
    "\n",
    "print('El elemento \"{}\" se repite {} vez'.format(software[lugar], software.count(software[lugar])))\n",
    "\n",
    "print('El elemento \"{}\" está en el lugar {}'.format(software[lugar], software.index(software[lugar])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tupla1 = sorted((4, 1, 9, 3), reverse=True)\n",
    "print(tupla1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Trucazo. Se puede ordenar la tupla dentro de la lista de acuerdo a la posición del elemento dentro de la tupla\n",
    "tupla2 = [('A', 1, 5), ('B', 3, 2), ('C', 2, 6)]\n",
    "\n",
    "tupla2_ordenada = sorted(tupla2, key=lambda x:x[1])\n",
    "print(tupla2_ordenada)\n",
    "\n",
    "otra_tupla_ordenada = sorted(palabras, key=lambda x:len(x))\n",
    "print(otra_tupla_ordenada)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Diccionarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curso = {'¿Qué curso es?':'Python para los no programadores',\n",
    "         '¿Quién da el curso?':'Javier',\n",
    "         '¿Cuántos alumnos hay?':1000,\n",
    "         '¿Calificación para pasar el curso?':[8.7, 9.3, 9.9],\n",
    "         '¿Es verdadero este curso?':True,\n",
    "         '¿El curso cumple con las espectativas?':None}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curso['¿Qué curso es?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curso['¿Quién da el curso?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curso['¿Cuántos alumnos hay?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curso['¿Calificación para pasar el curso?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curso['¿Es verdadero este curso?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curso['¿El curso cumple con las espectativas?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(curso.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(curso.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(curso.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Agregar valores al diccionario\n",
    "curso.update({'¿Los participantes están contentos?':'Si'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "curso"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[pandas - Python Data Analysis Library](https://pandas.pydata.org/)\n",
    "![pandas](https://upload.wikimedia.org/wikipedia/commons/thumb/e/ed/Pandas_logo.svg/245px-Pandas_logo.svg.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Vistazo rápido a Pandas No. 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(curso)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(1983)\n",
    "\n",
    "curso = {'Integrantes':['Javier', 'Olivia', 'Flora', 'Gabriel', 'Luis', 'Josué', 'Francisco', 'Mayra'],\n",
    "         'Edad':np.random.randint(18, 40, size=(8)),\n",
    "         'IQ':np.random.randint(70, 120, size=(8)),\n",
    "         'Categorica':np.random.randint(0, 1000, size=(8))} # Variable categorica PARES == SI IMPARES == NO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(curso)\n",
    "df.Edad.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### FIN del Vistazo rápido a Pandas No. 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### El TodoPoderoso bucle \"FOR\" con el inigualable condicional \"IF\" y el convenenciero \"WHILE\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### ¿Qué es un iterador? Un iterador se refiere al objeto que permite al programador recorrer un contenedor, particularmente listas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cerebro\n",
      "mente\n",
      "dualidad\n",
      "pensamiento\n"
     ]
    }
   ],
   "source": [
    "palabras = ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría']\n",
    "\n",
    "iterador = iter(palabras)\n",
    "\n",
    "print(next(iterador))\n",
    "print(next(iterador))\n",
    "print(next(iterador))\n",
    "print(next(iterador))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cerebro\n",
      "mente\n",
      "dualidad\n",
      "pensamiento\n",
      "pasión\n",
      "enojo\n",
      "alegría\n"
     ]
    }
   ],
   "source": [
    "palabras = ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría']\n",
    "\n",
    "for palabra in palabras:\n",
    "    print(palabra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cerebro\n",
      "mente\n",
      "dualidad\n",
      "pensamiento\n",
      "pasión\n",
      "enojo\n",
      "alegría\n"
     ]
    }
   ],
   "source": [
    "# También aplicamos el bucle \"for\" a los diccionarios\n",
    "\n",
    "otras_palabras = {'cerebro': 1, 'mente': 2, 'dualidad': 3, 'pensamiento':4, 'pasión':5, 'enojo':6, 'alegría':7}\n",
    "\n",
    "for keys in otras_palabras:\n",
    "    print(keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "for valores in otras_palabras.values():\n",
    "    print(valores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "otras_palabras = {'cerebro': 1, 'mente': 2, 'dualidad': 3, 'pensamiento':4, 'pasión':5, 'enojo':6, 'alegría':7}\n",
    "\n",
    "for keys in otras_palabras:\n",
    "    print(otras_palabras[keys])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "otras_palabras['cerebro']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keys = cerebro, valor = 1\n",
      "keys = mente, valor = 2\n",
      "keys = dualidad, valor = 3\n",
      "keys = pensamiento, valor = 4\n",
      "keys = pasión, valor = 5\n",
      "keys = enojo, valor = 6\n",
      "keys = alegría, valor = 7\n"
     ]
    }
   ],
   "source": [
    "for keys, values in otras_palabras.items():\n",
    "    print('keys = {}, valor = {}'.format(keys, values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5\n",
      "-4\n",
      "-3\n",
      "-2\n",
      "-1\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# funcion \"range\"\n",
    "for n in range(-5, 5):\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "2\n",
      "4\n",
      "6\n",
      "8\n",
      "10\n",
      "12\n",
      "14\n",
      "16\n",
      "18\n",
      "20\n",
      "22\n",
      "24\n",
      "26\n",
      "28\n",
      "30\n",
      "32\n",
      "34\n",
      "36\n",
      "38\n",
      "40\n",
      "42\n",
      "44\n",
      "46\n",
      "48\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "for n in range(0, 51, 2):\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-50, -48, -46, -44, -42, -40, -38, -36, -34, -32, -30, -28, -26, -24, -22, -20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50]\n"
     ]
    }
   ],
   "source": [
    "num = [n for n in range(-50, 51, 2)]\n",
    "print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2500, 2304, 2116, 1936, 1764, 1600, 1444, 1296, 1156, 1024, 900, 784, 676, 576, 484, 400, 324, 256, 196, 144, 100, 64, 36, 16, 4, 0, 4, 16, 36, 64, 100, 144, 196, 256, 324, 400, 484, 576, 676, 784, 900, 1024, 1156, 1296, 1444, 1600, 1764, 1936, 2116, 2304, 2500]\n"
     ]
    }
   ],
   "source": [
    "otro_num = map(lambda x: x * x, num)\n",
    "print(list(otro_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'i', 'e', 'i', 'a', 'u', 'e', 'a', 'a', 'e']\n"
     ]
    }
   ],
   "source": [
    "vocal = [v for v in 'Javier Villanueva Valle' if v in 'aeiou']\n",
    "print(vocal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 2, 3, 2, 4, 1, 3, 4, 4, 3]\n"
     ]
    }
   ],
   "source": [
    "conteo_vocal = [vocal.count(iteradores) for iteradores in vocal]\n",
    "print(conteo_vocal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['e', 'i', 'a', 'u']\n"
     ]
    }
   ],
   "source": [
    "conjunto_vocal = list(set(vocal))\n",
    "print(conjunto_vocal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "i\n",
      "e\n",
      "i\n",
      "a\n",
      "u\n",
      "e\n",
      "a\n",
      "a\n",
      "e\n"
     ]
    }
   ],
   "source": [
    "vocales = 'aeiou'\n",
    "for v in 'Javier Villanueva Valle':\n",
    "    if v in vocales:\n",
    "        print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "i\n",
      "e\n",
      "i\n",
      "a\n",
      "u\n",
      "e\n",
      "a\n",
      "a\n",
      "e\n"
     ]
    }
   ],
   "source": [
    "for v in 'Javier Villanueva Valle':\n",
    "    if v in 'aeiou':\n",
    "        print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "fragmento = \"\"\"\n",
    "Mario Benedetti\n",
    "Pedro y el Capitán (fragmento)\n",
    "\n",
    "Pedro, usted está muerto y yo también. De distintas muertes, claro. La mía es una muerte por trampa, por emboscada. \n",
    "Caí en la emboscada y ya no hay posible retroceso. Estoy entrampado. \n",
    "Si yo le dijera que no puedo abandonar esto, usted me diría que es natural porque sería abandonar el confort, los dos autos, etcétera. \n",
    "Y no es así. Todo eso lo dejaría sin remordimientos. Si no lo dejo es porque tengo miedo. Pueden hacer conmigo lo mismo que hacen, que hacemos con usted. \n",
    "Y usted seguramente me diría: «Bueno, ya ves, puede aguantarse.» Usted sí puede aguantarlo, porque tiene en qué creer, tiene a qué asirse. \n",
    "Yo no. Pero dentro de mi imposibilidad de rescatarme, me queda una solución intermedia. \n",
    "Ya sé que Inés y los chicos pueden un día llegar a odiarme, si se enteran con lujo de detalles de lo que hice y de lo que hago. \n",
    "Pero si todo esto lo hago, además, sin conseguir nada, como ha sido en su caso hasta ahora, no tengo justificación posible. \n",
    "Si usted muere sin nombrar un solo dato, para mí es la derrota total, la vergüenza total. Si en cambio dice algo, habrá también algo que me justifique. \n",
    "Ya mi crueldad no será gratuita, puesto que cumple su objetivo. Es sólo eso lo que le pido, lo que le suplico. \n",
    "Ya no cuatro nombres y apellidos, sino tan sólo uno. Y puede elegir: Gabriel o Rosario o Magdalena o Fermín. \n",
    "Uno solito, el que menos represente para usted; aquel al que usted le tenga menos afecto; incluso el que sea menos importante. \n",
    "No sé si me entiende: aquí no le estoy pidiendo una información para salvar al régimen, sino un dato para salvarme yo, o mejor dicho para salvar un poco de mí. \n",
    "Le estoy pidiendo la mediocre justificación de la eficacia, para no quedar ante Inés y los chicos como un sádico inútil,\n",
    "sino por lo menos como un sabueso eficaz, como un profesional redituable. De lo contrario, lo pierdo todo.\n",
    "(El Capitán da unos pasos hacia Pedro y cae de rodillas ante él.) \n",
    "Pedro, nos queda poco tiempo, muy poco tiempo. A usted y a mí. Pero usted se va y yo me quedo. \n",
    "Pedro, este es un ruego de un hombre deshecho. Usted no es inhumano. Usted es un hombre sensible. \n",
    "Usted es capaz de querer a la gente, de sufrir por la gente, de morir por la gente. \n",
    "Pedro, se lo ruego: diga un nombre y un apellido, nada más que un nombre y un apellido. \n",
    "A esto se ha reducido toda mi exigencia. Igual el triunfo será suyo.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nMario Benedetti\\nPedro y el Capitán (fragmento)\\n\\nPedro, usted está muerto y yo también. De distintas muertes, claro. La mía es una muerte por trampa, por emboscada. \\nCaí en la emboscada y ya no hay posible retroceso. Estoy entrampado. \\nSi yo le dijera que no puedo abandonar esto, usted me diría que es natural porque sería abandonar el confort, los dos autos, etcétera. \\nY no es así. Todo eso lo dejaría sin remordimientos. Si no lo dejo es porque tengo miedo. Pueden hacer conmigo lo mismo que hacen, que hacemos con usted. \\nY usted seguramente me diría: «Bueno, ya ves, puede aguantarse.» Usted sí puede aguantarlo, porque tiene en qué creer, tiene a qué asirse. \\nYo no. Pero dentro de mi imposibilidad de rescatarme, me queda una solución intermedia. \\nYa sé que Inés y los chicos pueden un día llegar a odiarme, si se enteran con lujo de detalles de lo que hice y de lo que hago. \\nPero si todo esto lo hago, además, sin conseguir nada, como ha sido en su caso hasta ahora, no tengo justificación posible. \\nSi usted muere sin nombrar un solo dato, para mí es la derrota total, la vergüenza total. Si en cambio dice algo, habrá también algo que me justifique. \\nYa mi crueldad no será gratuita, puesto que cumple su objetivo. Es sólo eso lo que le pido, lo que le suplico. \\nYa no cuatro nombres y apellidos, sino tan sólo uno. Y puede elegir: Gabriel o Rosario o Magdalena o Fermín. \\nUno solito, el que menos represente para usted; aquel al que usted le tenga menos afecto; incluso el que sea menos importante. \\nNo sé si me entiende: aquí no le estoy pidiendo una información para salvar al régimen, sino un dato para salvarme yo, o mejor dicho para salvar un poco de mí. \\nLe estoy pidiendo la mediocre justificación de la eficacia, para no quedar ante Inés y los chicos como un sádico inútil,\\nsino por lo menos como un sabueso eficaz, como un profesional redituable. De lo contrario, lo pierdo todo.\\n(El Capitán da unos pasos hacia Pedro y cae de rodillas ante él.) \\nPedro, nos queda poco tiempo, muy poco tiempo. A usted y a mí. Pero usted se va y yo me quedo. \\nPedro, este es un ruego de un hombre deshecho. Usted no es inhumano. Usted es un hombre sensible. \\nUsted es capaz de querer a la gente, de sufrir por la gente, de morir por la gente. \\nPedro, se lo ruego: diga un nombre y un apellido, nada más que un nombre y un apellido. \\nA esto se ha reducido toda mi exigencia. Igual el triunfo será suyo.\\n'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fragmento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Mario', 'Benedetti', 'Pedro', 'y', 'el', 'Capitán', '(fragmento)', 'Pedro,', 'usted', 'está', 'muerto', 'y', 'yo', 'también.', 'De', 'distintas', 'muertes,', 'claro.', 'La', 'mía', 'es', 'una', 'muerte', 'por', 'trampa,', 'por', 'emboscada.', 'Caí', 'en', 'la', 'emboscada', 'y', 'ya', 'no', 'hay', 'posible', 'retroceso.', 'Estoy', 'entrampado.', 'Si', 'yo', 'le', 'dijera', 'que', 'no', 'puedo', 'abandonar', 'esto,', 'usted', 'me', 'diría', 'que', 'es', 'natural', 'porque', 'sería', 'abandonar', 'el', 'confort,', 'los', 'dos', 'autos,', 'etcétera.', 'Y', 'no', 'es', 'así.', 'Todo', 'eso', 'lo', 'dejaría', 'sin', 'remordimientos.', 'Si', 'no', 'lo', 'dejo', 'es', 'porque', 'tengo', 'miedo.', 'Pueden', 'hacer', 'conmigo', 'lo', 'mismo', 'que', 'hacen,', 'que', 'hacemos', 'con', 'usted.', 'Y', 'usted', 'seguramente', 'me', 'diría:', '«Bueno,', 'ya', 'ves,', 'puede', 'aguantarse.»', 'Usted', 'sí', 'puede', 'aguantarlo,', 'porque', 'tiene', 'en', 'qué', 'creer,', 'tiene', 'a', 'qué', 'asirse.', 'Yo', 'no.', 'Pero', 'dentro', 'de', 'mi', 'imposibilidad', 'de', 'rescatarme,', 'me', 'queda', 'una', 'solución', 'intermedia.', 'Ya', 'sé', 'que', 'Inés', 'y', 'los', 'chicos', 'pueden', 'un', 'día', 'llegar', 'a', 'odiarme,', 'si', 'se', 'enteran', 'con', 'lujo', 'de', 'detalles', 'de', 'lo', 'que', 'hice', 'y', 'de', 'lo', 'que', 'hago.', 'Pero', 'si', 'todo', 'esto', 'lo', 'hago,', 'además,', 'sin', 'conseguir', 'nada,', 'como', 'ha', 'sido', 'en', 'su', 'caso', 'hasta', 'ahora,', 'no', 'tengo', 'justificación', 'posible.', 'Si', 'usted', 'muere', 'sin', 'nombrar', 'un', 'solo', 'dato,', 'para', 'mí', 'es', 'la', 'derrota', 'total,', 'la', 'vergüenza', 'total.', 'Si', 'en', 'cambio', 'dice', 'algo,', 'habrá', 'también', 'algo', 'que', 'me', 'justifique.', 'Ya', 'mi', 'crueldad', 'no', 'será', 'gratuita,', 'puesto', 'que', 'cumple', 'su', 'objetivo.', 'Es', 'sólo', 'eso', 'lo', 'que', 'le', 'pido,', 'lo', 'que', 'le', 'suplico.', 'Ya', 'no', 'cuatro', 'nombres', 'y', 'apellidos,', 'sino', 'tan', 'sólo', 'uno.', 'Y', 'puede', 'elegir:', 'Gabriel', 'o', 'Rosario', 'o', 'Magdalena', 'o', 'Fermín.', 'Uno', 'solito,', 'el', 'que', 'menos', 'represente', 'para', 'usted;', 'aquel', 'al', 'que', 'usted', 'le', 'tenga', 'menos', 'afecto;', 'incluso', 'el', 'que', 'sea', 'menos', 'importante.', 'No', 'sé', 'si', 'me', 'entiende:', 'aquí', 'no', 'le', 'estoy', 'pidiendo', 'una', 'información', 'para', 'salvar', 'al', 'régimen,', 'sino', 'un', 'dato', 'para', 'salvarme', 'yo,', 'o', 'mejor', 'dicho', 'para', 'salvar', 'un', 'poco', 'de', 'mí.', 'Le', 'estoy', 'pidiendo', 'la', 'mediocre', 'justificación', 'de', 'la', 'eficacia,', 'para', 'no', 'quedar', 'ante', 'Inés', 'y', 'los', 'chicos', 'como', 'un', 'sádico', 'inútil,', 'sino', 'por', 'lo', 'menos', 'como', 'un', 'sabueso', 'eficaz,', 'como', 'un', 'profesional', 'redituable.', 'De', 'lo', 'contrario,', 'lo', 'pierdo', 'todo.', '(El', 'Capitán', 'da', 'unos', 'pasos', 'hacia', 'Pedro', 'y', 'cae', 'de', 'rodillas', 'ante', 'él.)', 'Pedro,', 'nos', 'queda', 'poco', 'tiempo,', 'muy', 'poco', 'tiempo.', 'A', 'usted', 'y', 'a', 'mí.', 'Pero', 'usted', 'se', 'va', 'y', 'yo', 'me', 'quedo.', 'Pedro,', 'este', 'es', 'un', 'ruego', 'de', 'un', 'hombre', 'deshecho.', 'Usted', 'no', 'es', 'inhumano.', 'Usted', 'es', 'un', 'hombre', 'sensible.', 'Usted', 'es', 'capaz', 'de', 'querer', 'a', 'la', 'gente,', 'de', 'sufrir', 'por', 'la', 'gente,', 'de', 'morir', 'por', 'la', 'gente.', 'Pedro,', 'se', 'lo', 'ruego:', 'diga', 'un', 'nombre', 'y', 'un', 'apellido,', 'nada', 'más', 'que', 'un', 'nombre', 'y', 'un', 'apellido.', 'A', 'esto', 'se', 'ha', 'reducido', 'toda', 'mi', 'exigencia.', 'Igual', 'el', 'triunfo', 'será', 'suyo.']\n"
     ]
    }
   ],
   "source": [
    "mario_benedetti = fragmento.split()\n",
    "print(mario_benedetti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "preposiciones = ['a', 'durante', 'según', 'ante', 'en', 'sin', 'bajo', 'entre', 'so', 'cabe', 'hacia', 'sobre', 'con', 'hasta', 'tras', 'contra', 'mediante', \n",
    "                 'versus', 'de', 'para', 'vía', 'desde', 'por']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'durante', 'según', 'ante', 'en', 'sin', 'bajo', 'entre', 'so', 'cabe', 'hacia', 'sobre', 'con', 'hasta', 'tras', 'contra', 'mediante', 'versus', 'de', 'para', 'vía', 'desde', 'por']\n"
     ]
    }
   ],
   "source": [
    "print(preposiciones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "print(mario_benedetti.count('a'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preposiciones[0] == 'a'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "print(mario_benedetti.count(preposiciones[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "4\n",
      "durante\n",
      "0\n",
      "según\n",
      "0\n",
      "ante\n",
      "2\n",
      "en\n",
      "4\n",
      "sin\n",
      "3\n",
      "bajo\n",
      "0\n",
      "entre\n",
      "0\n",
      "so\n",
      "0\n",
      "cabe\n",
      "0\n",
      "hacia\n",
      "1\n",
      "sobre\n",
      "0\n",
      "con\n",
      "2\n",
      "hasta\n",
      "1\n",
      "tras\n",
      "0\n",
      "contra\n",
      "0\n",
      "mediante\n",
      "0\n",
      "versus\n",
      "0\n",
      "de\n",
      "12\n",
      "para\n",
      "6\n",
      "vía\n",
      "0\n",
      "desde\n",
      "0\n",
      "por\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for prepos in preposiciones:\n",
    "    print(prepos)#, end='-')\n",
    "    print(mario_benedetti.count(prepos))#, end='. ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 0, 0, 2, 4, 3, 0, 0, 0, 0, 1, 0, 2, 1, 0, 0, 0, 0, 12, 6, 0, 0, 5]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contar_preposiciones = []\n",
    "for prepos in preposiciones:\n",
    "    contar_preposiciones.append(mario_benedetti.count(prepos))\n",
    "\n",
    "contar_preposiciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'ante', 'en', 'sin', 'hacia', 'con', 'hasta', 'de', 'para', 'por']\n",
      " \n",
      "0        a\n",
      "1     ante\n",
      "2       en\n",
      "3      sin\n",
      "4    hacia\n",
      "5      con\n",
      "6    hasta\n",
      "7       de\n",
      "8     para\n",
      "9      por\n",
      "dtype: object\n",
      " \n",
      "       0\n",
      "0      a\n",
      "1   ante\n",
      "2     en\n",
      "3    sin\n",
      "4  hacia\n",
      "5    con\n",
      "6  hasta\n",
      "7     de\n",
      "8   para\n",
      "9    por\n"
     ]
    }
   ],
   "source": [
    "las_prepo = []\n",
    "\n",
    "for prepo in preposiciones:\n",
    "    if prepo in mario_benedetti:\n",
    "        las_prepo.append(prepo)\n",
    "\n",
    "print(las_prepo)\n",
    "print(' ')\n",
    "print(pd.Series(las_prepo))\n",
    "print(' ')\n",
    "print(pd.DataFrame(las_prepo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'durante', 'según', 'ante', 'en', 'sin', 'bajo', 'entre', 'so', 'cabe', 'hacia', 'sobre', 'con', 'hasta', 'tras', 'contra', 'mediante', 'versus', 'de', 'para', 'vía', 'desde', 'por'] [4, 0, 0, 2, 4, 3, 0, 0, 0, 0, 1, 0, 2, 1, 0, 0, 0, 0, 12, 6, 0, 0, 5]\n"
     ]
    }
   ],
   "source": [
    "las_prepos = []\n",
    "for prepos in preposiciones:\n",
    "    las_prepos.append(prepos)\n",
    "\n",
    "cantidad_prepos = []\n",
    "for prepos in preposiciones:\n",
    "    cantidad_prepos.append(mario_benedetti.count(prepos))\n",
    "\n",
    "print(las_prepos, cantidad_prepos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Preposiciones</th>\n",
       "      <th>Cant_Prepos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>durante</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>según</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ante</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>en</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sin</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bajo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>entre</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>cabe</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>hacia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sobre</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>con</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hasta</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>tras</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>contra</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mediante</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>versus</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>de</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>para</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>vía</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>desde</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>por</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Preposiciones  Cant_Prepos\n",
       "0              a            4\n",
       "1        durante            0\n",
       "2          según            0\n",
       "3           ante            2\n",
       "4             en            4\n",
       "5            sin            3\n",
       "6           bajo            0\n",
       "7          entre            0\n",
       "8             so            0\n",
       "9           cabe            0\n",
       "10         hacia            1\n",
       "11         sobre            0\n",
       "12           con            2\n",
       "13         hasta            1\n",
       "14          tras            0\n",
       "15        contra            0\n",
       "16      mediante            0\n",
       "17        versus            0\n",
       "18            de           12\n",
       "19          para            6\n",
       "20           vía            0\n",
       "21         desde            0\n",
       "22           por            5"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las_prepos = []\n",
    "cantidad_prepos = []\n",
    "\n",
    "for prepos in preposiciones:\n",
    "    las_prepos.append(prepos)\n",
    "    cantidad_prepos.append(mario_benedetti.count(prepos))\n",
    "\n",
    "ejem_prepos = pd.DataFrame()\n",
    "ejem_prepos['Preposiciones'] = las_prepos\n",
    "ejem_prepos['Cant_Prepos'] = cantidad_prepos\n",
    "ejem_prepos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'ante', 'en', 'sin', 'hacia', 'con', 'hasta', 'de', 'para', 'por']\n",
      " \n",
      "0        a\n",
      "1     ante\n",
      "2       en\n",
      "3      sin\n",
      "4    hacia\n",
      "5      con\n",
      "6    hasta\n",
      "7       de\n",
      "8     para\n",
      "9      por\n",
      "dtype: object\n",
      " \n",
      "       0\n",
      "0      a\n",
      "1   ante\n",
      "2     en\n",
      "3    sin\n",
      "4  hacia\n",
      "5    con\n",
      "6  hasta\n",
      "7     de\n",
      "8   para\n",
      "9    por\n",
      " \n",
      " \n"
     ]
    }
   ],
   "source": [
    "prepos = [prepo for prepo in preposiciones if prepo in mario_benedetti]\n",
    "#prepos = [prepo for prepo in preposiciones]\n",
    "print(prepos)\n",
    "print(' ')\n",
    "print(pd.Series(prepos))\n",
    "print(' ')\n",
    "print(pd.DataFrame(prepos))\n",
    "print(' ')\n",
    "print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 0, 0, 2, 4, 3, 0, 0, 0, 0, 1, 0, 2, 1, 0, 0, 0, 0, 12, 6, 0, 0, 5]\n",
      " \n",
      "0      4\n",
      "1      0\n",
      "2      0\n",
      "3      2\n",
      "4      4\n",
      "5      3\n",
      "6      0\n",
      "7      0\n",
      "8      0\n",
      "9      0\n",
      "10     1\n",
      "11     0\n",
      "12     2\n",
      "13     1\n",
      "14     0\n",
      "15     0\n",
      "16     0\n",
      "17     0\n",
      "18    12\n",
      "19     6\n",
      "20     0\n",
      "21     0\n",
      "22     5\n",
      "dtype: int64\n",
      " \n",
      "     0\n",
      "0    4\n",
      "1    0\n",
      "2    0\n",
      "3    2\n",
      "4    4\n",
      "5    3\n",
      "6    0\n",
      "7    0\n",
      "8    0\n",
      "9    0\n",
      "10   1\n",
      "11   0\n",
      "12   2\n",
      "13   1\n",
      "14   0\n",
      "15   0\n",
      "16   0\n",
      "17   0\n",
      "18  12\n",
      "19   6\n",
      "20   0\n",
      "21   0\n",
      "22   5\n"
     ]
    }
   ],
   "source": [
    "conteo = [mario_benedetti.count(prepos) for prepos in preposiciones]\n",
    "print(conteo)\n",
    "print(' ')\n",
    "print(pd.Series(conteo))\n",
    "print(' ')\n",
    "print(pd.DataFrame(conteo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'durante', 'según', 'ante', 'en', 'sin', 'bajo', 'entre', 'so', 'cabe', 'hacia', 'sobre', 'con', 'hasta', 'tras', 'contra', 'mediante', 'versus', 'de', 'para', 'vía', 'desde', 'por']\n",
      "['El', 'el', 'La', 'la', 'Los', 'los', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]\n",
      "23\n",
      "23\n"
     ]
    }
   ],
   "source": [
    "print(preposiciones)\n",
    "articulo = ['El', 'el', 'La', 'la', 'Los', 'los', np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]\n",
    "print(articulo)\n",
    "print(len(preposiciones))\n",
    "print(len(articulo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>De_Otro_DF_Preposiciones</th>\n",
       "      <th>De_Otro_DF_Cant_Prepos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>durante</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ante</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>en</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sin</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bajo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>entre</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>so</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>cabe</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>hacia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sobre</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>con</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hasta</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>tras</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>contra</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mediante</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>versus</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>de</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>para</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>vía</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>desde</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>por</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   De_Otro_DF_Preposiciones  De_Otro_DF_Cant_Prepos\n",
       "0                         a                       4\n",
       "1                   durante                       0\n",
       "2                     según                       0\n",
       "3                      ante                       2\n",
       "4                        en                       4\n",
       "5                       sin                       3\n",
       "6                      bajo                       0\n",
       "7                     entre                       0\n",
       "8                        so                       0\n",
       "9                      cabe                       0\n",
       "10                    hacia                       1\n",
       "11                    sobre                       0\n",
       "12                      con                       2\n",
       "13                    hasta                       1\n",
       "14                     tras                       0\n",
       "15                   contra                       0\n",
       "16                 mediante                       0\n",
       "17                   versus                       0\n",
       "18                       de                      12\n",
       "19                     para                       6\n",
       "20                      vía                       0\n",
       "21                    desde                       0\n",
       "22                      por                       5"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "otro_ejem_prepos = pd.DataFrame()\n",
    "otro_ejem_prepos['De_Otro_DF_Preposiciones'] = [prepos for prepos in preposiciones]\n",
    "otro_ejem_prepos['De_Otro_DF_Cant_Prepos'] = [mario_benedetti.count(prepos) for prepos in preposiciones]\n",
    "#otro_ejem_prepos['Artículos'] = [art for art in articulo]\n",
    "#otro_ejem_prepos['Cuentame_los_Articulos'] = [mario_benedetti.count(art) for art in articulo]\n",
    "otro_ejem_prepos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f946b50>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "otro_ejem_prepos.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x11fe3ee10>,\n",
       "  <matplotlib.axis.XTick at 0x11fe3edd0>,\n",
       "  <matplotlib.axis.XTick at 0x11fe54950>,\n",
       "  <matplotlib.axis.XTick at 0x11fe6cc50>,\n",
       "  <matplotlib.axis.XTick at 0x11fe73350>,\n",
       "  <matplotlib.axis.XTick at 0x11fe73810>,\n",
       "  <matplotlib.axis.XTick at 0x11fe73d90>,\n",
       "  <matplotlib.axis.XTick at 0x11fe5e8d0>,\n",
       "  <matplotlib.axis.XTick at 0x11fe7a310>,\n",
       "  <matplotlib.axis.XTick at 0x11fe7a410>,\n",
       "  <matplotlib.axis.XTick at 0x11fe825d0>,\n",
       "  <matplotlib.axis.XTick at 0x11fe82b10>,\n",
       "  <matplotlib.axis.XTick at 0x11fe82450>,\n",
       "  <matplotlib.axis.XTick at 0x11fe7a610>,\n",
       "  <matplotlib.axis.XTick at 0x11fe737d0>,\n",
       "  <matplotlib.axis.XTick at 0x11fe6cf90>,\n",
       "  <matplotlib.axis.XTick at 0x11fe8c390>,\n",
       "  <matplotlib.axis.XTick at 0x11fe8c8d0>,\n",
       "  <matplotlib.axis.XTick at 0x11fe8ce10>,\n",
       "  <matplotlib.axis.XTick at 0x11fe93390>,\n",
       "  <matplotlib.axis.XTick at 0x11fe938d0>,\n",
       "  <matplotlib.axis.XTick at 0x11fe93e10>,\n",
       "  <matplotlib.axis.XTick at 0x11fe93f90>],\n",
       " [Text(0, 0, 'a'),\n",
       "  Text(0, 0, 'durante'),\n",
       "  Text(0, 0, 'según'),\n",
       "  Text(0, 0, 'ante'),\n",
       "  Text(0, 0, 'en'),\n",
       "  Text(0, 0, 'sin'),\n",
       "  Text(0, 0, 'bajo'),\n",
       "  Text(0, 0, 'entre'),\n",
       "  Text(0, 0, 'so'),\n",
       "  Text(0, 0, 'cabe'),\n",
       "  Text(0, 0, 'hacia'),\n",
       "  Text(0, 0, 'sobre'),\n",
       "  Text(0, 0, 'con'),\n",
       "  Text(0, 0, 'hasta'),\n",
       "  Text(0, 0, 'tras'),\n",
       "  Text(0, 0, 'contra'),\n",
       "  Text(0, 0, 'mediante'),\n",
       "  Text(0, 0, 'versus'),\n",
       "  Text(0, 0, 'de'),\n",
       "  Text(0, 0, 'para'),\n",
       "  Text(0, 0, 'vía'),\n",
       "  Text(0, 0, 'desde'),\n",
       "  Text(0, 0, 'por')])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "otro_ejem_prepos.set_index('De_Otro_DF_Preposiciones').plot(figsize=(10, 6))\n",
    "plt.xticks(range(len(otro_ejem_prepos['De_Otro_DF_Preposiciones'].tolist())), otro_ejem_prepos['De_Otro_DF_Preposiciones'].tolist(), rotation='vertical', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Condicional \"if\"\n",
    "![IF](https://yusout.com/wp-content/uploads/2019/03/11010974_10206432444145331_4692261788422933939_n_10206432444145331.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(bool(0))\n",
    "print(bool(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "si\n",
      "si\n",
      "si\n",
      "si, cuando x & y vale lo mismo\n",
      "si con &, cuando x & y vale lo mismo\n"
     ]
    }
   ],
   "source": [
    "x = 0\n",
    "y = 5\n",
    "\n",
    "if x < y:                            # Verdadero\n",
    "    print('si')\n",
    "\n",
    "\n",
    "if y < x:                            # Falso\n",
    "    print('si')\n",
    "\n",
    "\n",
    "if x:                                # Falso\n",
    "    print('si')\n",
    "\n",
    "if y:                                # Verdadero\n",
    "    print('si')\n",
    "\n",
    "\n",
    "\n",
    "if x or y:                           # Verdadero\n",
    "    print('si')\n",
    "\n",
    "\n",
    "if x and y:                          # Falso\n",
    "    print('si')\n",
    "\n",
    "if x & y:                            # Falso\n",
    "    print('si con &')\n",
    "\n",
    "x = 5; y = 5\n",
    "if x and y:                          # Verdadero\n",
    "    print('si, cuando x & y vale lo mismo')\n",
    "\n",
    "if x & y:                            # Verdadero\n",
    "    print('si con &, cuando x & y vale lo mismo')\n",
    "\n",
    "\n",
    "if 'aie' in 'Javier':                # Verdadero\n",
    "    print('si')\n",
    "\n",
    "\n",
    "if 'libertad' in ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría']:  # Falso\n",
    "    print('si')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['a', 'durante', 'según', 'ante', 'en', 'sin', 'bajo', 'entre', 'so', 'cabe', 'hacia', 'sobre', 'con', 'hasta', 'tras', 'contra', 'mediante', 'versus', 'de', 'para', 'vía', 'desde', 'por']\n"
     ]
    }
   ],
   "source": [
    "print(preposiciones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No está\n",
      "Si\n"
     ]
    }
   ],
   "source": [
    "if not 'libertad' in preposiciones:\n",
    "    print('No está')\n",
    "\n",
    "if 'desde' in preposiciones:\n",
    "    print('Si')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La expresión es verdadera\n",
      "Ejecutando la condición en la lista\n",
      "...\n",
      "Hecho.\n",
      "Después de la condicional\n"
     ]
    }
   ],
   "source": [
    "if not 'libertad' in ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría']:\n",
    "    print('La expresión es verdadera')\n",
    "    print('Ejecutando la condición en la lista')\n",
    "    print('...')\n",
    "    print('Hecho.')\n",
    "print('Después de la condicional')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Condicional es True\n",
      "Condición interna 1\n",
      "Entre las condiciones Internas\n",
      "Condición interna 2\n",
      "Fin de la 1ra condición\n",
      "Después de la 1ra condición\n"
     ]
    }
   ],
   "source": [
    "# ¿Se puede ejecutar las lineas?                Si    No\n",
    "#                                              ---    --\n",
    "if 'foo' in ['foo', 'bar', 'baz']:           #  x\n",
    "    print('Condicional es True')             #  x\n",
    "\n",
    "    if not 10 > 20:                              #  x\n",
    "        print('Condición interna 1')         #        x\n",
    "\n",
    "    print('Entre las condiciones Internas')  #  x\n",
    "\n",
    "    if 10 < 20:                              #  x\n",
    "        print('Condición interna 2')         #  x\n",
    "\n",
    "    print('Fin de la 1ra condición')         #  x\n",
    "print('Después de la 1ra condición')         #  x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2do conjunto)\n",
      "x es grande\n"
     ]
    }
   ],
   "source": [
    "x = 100\n",
    "\n",
    "if x < 50:\n",
    "    print('(1er conjunto)')\n",
    "    print('x es pequeño')\n",
    "else:\n",
    "    print('(2do conjunto)')\n",
    "    print('x es grande')\n",
    "    \n",
    "## ¿Cómo le hacemos para que se cumpla la segunda condición?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hola Gabriel\n"
     ]
    }
   ],
   "source": [
    "name = 'Gabriel'\n",
    "\n",
    "if name == 'Flora':\n",
    "    print('Hola Flora')\n",
    "elif name == 'Olivia':\n",
    "    print('Hola Olivia')\n",
    "elif name == 'Luis':\n",
    "    print('Hola Luis')\n",
    "elif name == 'Mayra':\n",
    "    print('Hola Mayra')\n",
    "elif name == 'Josué':\n",
    "    print('Hola Josué')\n",
    "elif name == 'Gabriel':\n",
    "    print('Hola Gabriel')\n",
    "else:\n",
    "    print(\"¡No tengo ni la más remota idea de quien seas!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "if 'P' in 'Python': print('1'); print('2'); print('3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "if 'a' in 'Python': print('1'); print('2'); print('3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Julia\n",
      "R\n"
     ]
    }
   ],
   "source": [
    "x = 100\n",
    "if x == 1: print('Curso'); print('de'); print('Python')\n",
    "elif x == 2: print('Para'); print('Principiantes')\n",
    "else: print('Julia'); print('R')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adulto\n"
     ]
    }
   ],
   "source": [
    "# Con operadores aritméticos\n",
    "edad = 18\n",
    "s = 'Menor' if edad < 18 else 'Adulto'\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "foo\n"
     ]
    }
   ],
   "source": [
    "if True:\n",
    "print('foo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Julia\n",
      "R\n"
     ]
    }
   ],
   "source": [
    "x = 3\n",
    "if x == 1: \n",
    "    print('Curso') \n",
    "    print('de') \n",
    "    print('Python')\n",
    "elif x == 2: \n",
    "    print('Para')\n",
    "    print('Principiantes')\n",
    "else: \n",
    "    print('Julia')\n",
    "    print('R')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bucle \"while\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3 sumandole 2\n",
      " \n",
      "3\n",
      "5 sumandole 2\n",
      " \n",
      "5\n",
      "7 sumandole 2\n",
      " \n"
     ]
    }
   ],
   "source": [
    "a = 1\n",
    "while a < 6:\n",
    "    print(a)\n",
    "    a += 2\n",
    "    print(a, 'sumandole 2')\n",
    "    print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "3\n",
      "2\n",
      "1\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "n = 5\n",
    "while n > 0:\n",
    "    n -= 1\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 0\n",
    "while n > 0:\n",
    "    n -= 1\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "4\n",
      "4\n",
      "3\n",
      "3\n",
      "Ciclo terminado.\n"
     ]
    }
   ],
   "source": [
    "n = 5\n",
    "while n > 0:\n",
    "    print(n)\n",
    "    n -= 1\n",
    "    if n == 2:\n",
    "        break\n",
    "    print(n)\n",
    "print('Ciclo terminado.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "3\n",
      "1\n",
      "0\n",
      "Ciclo terminado.\n"
     ]
    }
   ],
   "source": [
    "n = 5\n",
    "while n > 0:\n",
    "    n -= 1\n",
    "    if n == 2:\n",
    "        continue\n",
    "    print(n)\n",
    "print('Ciclo terminado.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "3\n",
      "2\n",
      "1\n",
      "0\n",
      "Ciclo terminado.\n"
     ]
    }
   ],
   "source": [
    "n = 5\n",
    "while n > 0:\n",
    "    n -= 1\n",
    "    print(n)\n",
    "else:\n",
    "    print('Ciclo terminado.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "3\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "n = 5\n",
    "while n > 0:\n",
    "    #print(n)\n",
    "    n -= 1\n",
    "    print(n)\n",
    "    if n == 2:\n",
    "        break\n",
    "else:\n",
    "    print('Ciclo terminado.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = ['cerebro', 'mente', 'dualidad', 'pensamiento', 'pasión', 'enojo', 'alegría']\n",
    "s = 'alegría'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alegría está en la lista.\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "while i < len(a):\n",
    "    if a[i] == s:\n",
    "        # Se está buscando la palabra declarada en \"s\"\n",
    "        print(s, 'está en la lista.')\n",
    "        break\n",
    "    i += 1\n",
    "else:\n",
    "    # Cuando no se encuentra la palabra declarada en \"s\"\n",
    "    print(s, 'no se encontró en la lista.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mientras cumpla con la condición y lo detengas (break) se convertirá en un ciclo infinito\n",
    "while True:\n",
    "    print('foo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "3\n",
      "2\n",
      "1\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# También se puede en una sola linea\n",
    "n = 5\n",
    "while n > 0: n -= 1; print(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Integración"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_aleatorios = range(-51, 51)\n",
    "for num in list(num_aleatorios):\n",
    "    if (num % 2 == 0):\n",
    "        print('El número {} es par'.format(num))\n",
    "    elif (num % 5 == 0):\n",
    "        print('El número {} dividido entre 5 tiene como resultado {} con residuo {} es impar'.format(num, num / 5, num % 5))\n",
    "    elif (num % 9 == 0):\n",
    "        print('El número {} dividido entre 9 es igual a {} y tiene como residuo {}'.format(num, num / 9, num % 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generación de una dataset\n",
    "integrantes = ['Javier', 'Olivia', 'Flora', 'Gabriel', 'Luis', 'Josué', 'Francisco', 'Mayra']\n",
    "unos_numeros = list(map(lambda x: len(x), integrantes))\n",
    "otros_numeros = list(map(lambda x: x * max(unos_numeros), unos_numeros))\n",
    "mas_numeros = list(map(lambda x: x ** x, unos_numeros))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Javier', 'Olivia', 'Flora', 'Gabriel', 'Luis', 'Josué', 'Francisco', 'Mayra']\n",
      "[6, 6, 5, 7, 4, 5, 9, 5]\n",
      "[54, 54, 45, 63, 36, 45, 81, 45]\n",
      "[46656, 46656, 3125, 823543, 256, 3125, 387420489, 3125]\n"
     ]
    }
   ],
   "source": [
    "print(integrantes)\n",
    "print(unos_numeros)\n",
    "print(otros_numeros)\n",
    "print(mas_numeros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Integrantes</th>\n",
       "      <th>Unos Números</th>\n",
       "      <th>Otros Números</th>\n",
       "      <th>Más Números</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Javier</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>46656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Olivia</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>46656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flora</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gabriel</td>\n",
       "      <td>7</td>\n",
       "      <td>63</td>\n",
       "      <td>823543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Luis</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Josué</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Francisco</td>\n",
       "      <td>9</td>\n",
       "      <td>81</td>\n",
       "      <td>387420489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mayra</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>3125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Integrantes  Unos Números  Otros Números  Más Números\n",
       "0      Javier             6             54        46656\n",
       "1      Olivia             6             54        46656\n",
       "2       Flora             5             45         3125\n",
       "3     Gabriel             7             63       823543\n",
       "4        Luis             4             36          256\n",
       "5       Josué             5             45         3125\n",
       "6   Francisco             9             81    387420489\n",
       "7       Mayra             5             45         3125"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ejemplo_1 = pd.DataFrame()\n",
    "\n",
    "ejemplo_1['Integrantes'] = integrantes\n",
    "ejemplo_1['Unos Números'] = unos_numeros\n",
    "ejemplo_1['Otros Números'] = otros_numeros\n",
    "ejemplo_1['Más Números'] = mas_numeros\n",
    "\n",
    "ejemplo_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(2016)\n",
    "\n",
    "curso = {'Integrantes':['Javier', 'Olivia', 'Flora', 'Gabriel', 'Luis', 'Josué', 'Francisco', 'Mayra'],\n",
    "         'Edad':np.random.randint(18, 40, size=(8)),\n",
    "         'IQ':np.random.randint(70, 120, size=(8)),\n",
    "         'Num_Gotas_Agua_en_el_Cuerpo':np.random.randint(0, 9999999, size=(8))} # Variable categorica PARES == SI IMPARES == NO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Integrantes': ['Javier',\n",
       "  'Olivia',\n",
       "  'Flora',\n",
       "  'Gabriel',\n",
       "  'Luis',\n",
       "  'Josué',\n",
       "  'Francisco',\n",
       "  'Mayra'],\n",
       " 'Edad': array([29, 28, 37, 31, 25, 20, 29, 21]),\n",
       " 'IQ': array([110, 113, 114,  81, 110, 109, 111, 104]),\n",
       " 'Num_Gotas_Agua_en_el_Cuerpo': array([7865843,  356324, 1944958, 7830766,   82273, 8914066, 5370594,\n",
       "        3472328])}"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "curso = pd.DataFrame(curso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Integrantes</th>\n",
       "      <th>Edad</th>\n",
       "      <th>IQ</th>\n",
       "      <th>Num_Gotas_Agua_en_el_Cuerpo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Javier</td>\n",
       "      <td>29</td>\n",
       "      <td>110</td>\n",
       "      <td>7865843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Olivia</td>\n",
       "      <td>28</td>\n",
       "      <td>113</td>\n",
       "      <td>356324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flora</td>\n",
       "      <td>37</td>\n",
       "      <td>114</td>\n",
       "      <td>1944958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gabriel</td>\n",
       "      <td>31</td>\n",
       "      <td>81</td>\n",
       "      <td>7830766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Luis</td>\n",
       "      <td>25</td>\n",
       "      <td>110</td>\n",
       "      <td>82273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Josué</td>\n",
       "      <td>20</td>\n",
       "      <td>109</td>\n",
       "      <td>8914066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Francisco</td>\n",
       "      <td>29</td>\n",
       "      <td>111</td>\n",
       "      <td>5370594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mayra</td>\n",
       "      <td>21</td>\n",
       "      <td>104</td>\n",
       "      <td>3472328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Integrantes  Edad   IQ  Num_Gotas_Agua_en_el_Cuerpo\n",
       "0      Javier    29  110                      7865843\n",
       "1      Olivia    28  113                       356324\n",
       "2       Flora    37  114                      1944958\n",
       "3     Gabriel    31   81                      7830766\n",
       "4        Luis    25  110                        82273\n",
       "5       Josué    20  109                      8914066\n",
       "6   Francisco    29  111                      5370594\n",
       "7       Mayra    21  104                      3472328"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "curso['Gotas_Importantes'] = curso[['Num_Gotas_Agua_en_el_Cuerpo']].applymap(lambda x: 'Si' if (x % 2 == 0) else 'No')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Integrantes</th>\n",
       "      <th>Edad</th>\n",
       "      <th>IQ</th>\n",
       "      <th>Num_Gotas_Agua_en_el_Cuerpo</th>\n",
       "      <th>Gotas_Importantes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Javier</td>\n",
       "      <td>29</td>\n",
       "      <td>110</td>\n",
       "      <td>7865843</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Olivia</td>\n",
       "      <td>28</td>\n",
       "      <td>113</td>\n",
       "      <td>356324</td>\n",
       "      <td>Si</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flora</td>\n",
       "      <td>37</td>\n",
       "      <td>114</td>\n",
       "      <td>1944958</td>\n",
       "      <td>Si</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gabriel</td>\n",
       "      <td>31</td>\n",
       "      <td>81</td>\n",
       "      <td>7830766</td>\n",
       "      <td>Si</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Luis</td>\n",
       "      <td>25</td>\n",
       "      <td>110</td>\n",
       "      <td>82273</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Josué</td>\n",
       "      <td>20</td>\n",
       "      <td>109</td>\n",
       "      <td>8914066</td>\n",
       "      <td>Si</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Francisco</td>\n",
       "      <td>29</td>\n",
       "      <td>111</td>\n",
       "      <td>5370594</td>\n",
       "      <td>Si</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mayra</td>\n",
       "      <td>21</td>\n",
       "      <td>104</td>\n",
       "      <td>3472328</td>\n",
       "      <td>Si</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Integrantes  Edad   IQ  Num_Gotas_Agua_en_el_Cuerpo Gotas_Importantes\n",
       "0      Javier    29  110                      7865843                No\n",
       "1      Olivia    28  113                       356324                Si\n",
       "2       Flora    37  114                      1944958                Si\n",
       "3     Gabriel    31   81                      7830766                Si\n",
       "4        Luis    25  110                        82273                No\n",
       "5       Josué    20  109                      8914066                Si\n",
       "6   Francisco    29  111                      5370594                Si\n",
       "7       Mayra    21  104                      3472328                Si"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fee11d0>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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eQdoTgWsyeA3x1P5Ojg8M4876COozEsHr3NB1+vKC8kI1MJ9lfCLOviP9Z87Y29HazcDIZJAKVRXz9msWJIJUgAXl6RWkptIwr5RHP7GRP3pwJx/5/k7+7J1X84Eb6rwuKyskO1j9GPiBmf1vJpvXG4DtF36ISOYIh4L8fN8x4nF3wQF/kl1iAyPsO9LPf3/ryvPepyDXT6iqmFBV8ZS3j47HOdo39LrQ1d4zOb37WP8w8bOClxnMKy04E7pqK4vOLDPWVhaxsKIgo+fyjU3E2dvRd+aMvZ1tPZxMBKn66mLece1CNtRPLu3NK8vM3TNqSgv44T0b+NRDz/Knj+3jUPcgX3jblXpvmmWXMm7hIeANQJWZtQNfBLqBrwLVwE/N7Dnn3Fudc/vM7EfAi8A48IlUOiNQZLY1hgL8687DvHJ8gCsXaGSHTHqiOQbApobqaf+OvBwfS4PFLA1OHbzGJuIc6xvmcOKI16vBa5AdbT38ZM9RJuLndl3UlOZP0d+V+LmiMK32Ph2biLOnve/MEamdbd0Mjk7+87O8poTbrlt4ZpZUTYYGqakU5eXwzQ+s489/so9vPdFKe88QX3nPdWn13zbdXMpZge87z02Pnuf+fwX81UyKEklX4frJPqvtrd0KVnJGJBojWJzH1bM4Hy/X72NxoOi8jdXjE3GO9Q+fOcp1ur+rvWeIZw/38LMXjjL+muBVVZI/5RGvxZWFLKooojDPu3+cR8fj7GnvPStI9TA0NhmkVswr4d1rawmHgjSGAlk/5NnvM770zqtZEizmL3/6Ise+tY1v/cE6qkqy+3WZLZq8LpJEtZVFLKoopKm1i7s31nldjqSAeHxyzMJNDVWeLsHk+H2Jo1FTB6+JuOP4meB11lJj7yAvdPTxi33HGJs4N3gFi/OmPKPx9P8HxfnJ+ydmZHyC5w/3ndkeZtfBHobH4gBcMb+U96xfTDgUoDEUIKjA8DpmxodvCrGoooBP//A57rjvab77ofUsqy7xurSMo2AlkmThUIBIcwznnOezbcR7Lx7tp+vU6IyWAeeCPzH+YWFFIY2JM1zPFo87TgyMnDnK1dH76hGvF4/288uXjjM6Hj/nMZVFuecsLZ4JYYHJy6UF5x9gOTw2wXOHE0ekWrrZfaiHkfE4ZnDF/DLe17jkzBGpQLFmEV6qW1ct4If3FPBHD+7kjvue5lt/sG7K/94yfQpWIkkWrg/wyLMdHIidYnmNPg1mu63Ryf6qm1dUeVzJzPh8xvzyAuaXF7Cu7vW3x+OOzpMjHD59xKv31eb66PEBfv3yCUZeE7zKC3NfPaOxYjKA9Q6N0dTSxbOHexlNBKmrFpTx/g1LzxyR0lDnmbl+SSWP3nsjH/zedt7/7Sb+9q7V3HbdlCMnZRoUrESSrDEUBCb7rBSsZGs0xlULyqgpzeyGaZ/PqCkroKasYMr9Mp1zdJ4cfU3omjzi1RI7RSTaydDYBD6DVYvKufuGpYRDQdaHAtqaZRYsCRbxyMc3cs8/7eLTP3yO9p4h7n3DMh1lTwIFK5EkqwsWUVOaT1NrF78Xnt1dBSS1DQyPsftgj+aaMdnjU12aT3VpPtcvmTp4dZ8aJS/Hd8ElQkmeiqI8/unDjXzu4T387S9e4XD3IH/xrlUaPDtDClYiSWZmNIYCNLV0q88qyz1zoIvxuGNTmi8DzgUzU9O5B/Jz/PzDe65jSaCIr/56P0f6hvna712vcDsDiqUisyBcH+RY/zCHu4e8LkU8FGmOUZTnZ91SNQdL6jIz/p+3rOTLd17DU/s7uesbz3C0T+9d06VgJTILTu8buK21y+NKxCvOObZGY2xcFtTWMpIW3rN+Cd/94Hrae4Z419eeYt+RPq9LSkv6v11kFjTUlBAozmN7a7fXpYhH2roGOdw9xKYVqT1mQeRsm1ZU8/DHb8Bnxu9+4xl+88oJr0tKOwpWIrPAzGisC9CkI1ZZKxKd+TY2Il64Yn4Z//aJG1kaLObDD+7kB02HvC4prShYicySxlCAw91DHOlVr0I2ikRjLAkUUXeeTZVFUtm8sgJ+9LEb2NRQxf949AW+/POXib9myyOZmoKVyCw5e99AyS4j4xM809KlswElrZXk5/CtP1jH74eX8PXfHOBTP3yW4cR+jHJ+ClYis+SK+WWUFuRoOTAL7WrrYXB0gs0rarwuRWRGcvw+/vJdq/jC267g3/cc5f3fbqLn1KjXZaU0BSuRWeL3ne6z0hGrbLO1OUaOz7hhWdDrUkRmzMz46OZlfO331rCno487vv40bZ2nvC4rZSlYicyixlCAltgpTgwMe12KzKFItJO1SyspydcMZskcv716AQ99JEzv4Ch3fP1pdh3s8bqklKRgJTKLwvWTRyx2tOoNKFucGBjmpaP9GrMgGWnt0gCP3nsjZQU5vO9b2/jZC0e9LinlKFiJzKKrF5ZRlOdXn1UWeSLaCcBmBSvJUHVVxTxy742sXlTOvf+ym/sjB3BOZwyepmAlMoty/T7WLq3UmYFZZGs0RlVJHlctKPO6FJFZEyjO45//KMxvX7OAv/7Zy/zpY3sZn4h7XVZKULASmWUb6oO8fGxAZ9JkgXjc8eT+Tm5uqMbn0+bbktkKcv189X3X89HN9fzztkN85Ps7OTUy7nVZnlOwEplljYl9A7e36ahVptt7pI/uU6OaXyVZw+czvvC2K/nLd61iazTG737zGY73Z/fJOgpWIrNsdW05+Tk+LQdmgdPb2NysbWwky7x/w1K+c/d6WjtPcfvXnuLlY/1el+QZBSuRWZaf4+f6JRVqYM8CkWgnVy8so6ok3+tSRObcG6+o4UcfvYEJ57jr68/wZHOn1yV5QsFKZA6EQ0FePNJP//CY16XILOkfHmPXoR6dDShZbdWich6990YWVRbywe9u50c7Dntd0pxTsBKZA+FQgLib3OpEMtPT+7uYiDvNr5Kst7CikP/7sRu4YVmQz23Zw9//5ytZNY5BwUpkDly/pJJcv2l7mwwWaY5RnOdnzZJKr0sR8VxpQS4PfHA971m3mK/+ej9//K/PMTKeHRs4a78FkTlQmOfn2lr1WWUq5xyRaIwbllWRl6PPqyIwOcfvb+68hiXBIv72F69wtG+Y+z+wjvKiXK9Lm1V6BxCZI42hAC+09zE4qjkvmaa18xTtPUNs1pgFkXOYGZ9443L+8b3X8eyhXu74+lMc7h70uqxZpWAlMkfC9UHG447dB3u9LkWSbGtizMLmFTUeVyKSmm67bhH/9OFGOk+Ocvt9T/Hc4cx9H1SwEpkja5dW4veZlgMzUCQaoy5YxJJgkdeliKSscH2QR+7dSGGen/fe/ww/33vM65JmhYKVyBwpyc9h1cIyNbBnmJHxCba1dOtsQJFLsKy6hEfvvZEr5pfx8X/ZxXeebPW6pKRTsBKZQ42hAM8d7mV4LDvOjskGO9t6GBqbYJOmrYtckqqSfB76yAbectU8/uLfX+RLP97HRDxzxjEoWInMoXAoyOh4nOczuL8g20SiMXL9xg3Lgl6XIpI2CvP83Pf7a/mjm0J87+k2PvpPuzLmxB4FK5E5tL4ugBlaDswgW6Mx1i0NUJyv6TUil8PvM/7kHVfxZ++8ml+/fJz33r+NEwPpv4GzgpXIHCovyuWK+WXakDlDHO8f5uVjA+qvEpmBuzfWcf8H1tF8/CS3f+1pmo8PeF3SjChYicyxcCjAroM9jE3EvS5FZiiSGLOwSfOrRGbkTVfN40cfvYHRiTh3fP1pnj6Qvhs4K1iJzLFwKMDQ2AR72vu8LkVmKNLcSVVJPlfOL/O6FJG0d01tOY/eu5H5ZQXc/cB2Htnd7nVJ03LRYGVmD5jZCTPbe9Z1ATP7pZk1J75XJq6vM7MhM3su8fWN2SxeJB01hgIAWg5McxNxx5PNMTY1VOHzmdfliGSE2soiHv74RtYtDfDZHz3PP/6qOe02cL6UI1bfA259zXWfBx53zjUAjycun3bAOXdd4utjySlTJHMES/JZXlOiQaFp7oWOPnoGx9i8Uv1VIslUXpjLg3/YyB1rFvGVX0X57w/vYXQ8fVonLhqsnHMR4LUfrW8DHkz8/CDwriTXJZLRwqEAO9t6Mmp2S7aJRGOYwU3L1V8lkmx5OT7+/q5r+cybGnh4Vzsf+t52+obGvC7rkky3x2qec+4oQOL72RtkhczsWTPbamY3n+8XmNk9ZrbTzHbGYrFpliGSnsL1QU6OjPPikX6vS5FpikRjrFpYTrAk3+tSRDKSmfGZN63g7+66lqaWbu76xtO096T+Bs7Jbl4/Cixxzl0PfBb4gZlN2dXpnLvfObfOObeuulqH0iW7hBN9VloOTE/9w2M8e7hXZwOKzIF3r63l+3/YyNG+YW6/72leSPETf6YbrI6b2QKAxPcTAM65EedcV+LnXcABYEUyChXJJPPKCqgLFmlQaJp6en8nE3GnbWxE5sjG5VVs+fhG8vw+fvebz/D4S8e9Lum8phusfgzcnfj5buAxADOrNjN/4ud6oAFomWmRIpmoMRRgR1s3cfVZpZ2t0Rgl+TmsWVrpdSkiWWPFvFIe/cRGlteU8JHv7+SfnmnzuqQpXcq4hYeAZ4CVZtZuZh8G/gZ4s5k1A29OXAbYBOwxs+eBh4GPOef0kVxkCuFQkN7BMaIn0nvKcLZxzhGJdrJxWZBcv0YBisylmtIC/vWjG/itK2r408f28Vc/fTHlPpxedHMr59z7znPTLVPcdwuwZaZFiWSD0/Osmlq6uUIDJtPGgdgpOnqH+PgblnldikhWKsrL4ZsfWMef/2Qf33qilfaeIb7ynusoyPV7XRqgyesinlkcKGJRRaEGhaaZ09vYbNb+gCKe8fuML73zav70HVfx833HeN+3ttF5csTrsgAFKxFPNYYCNLV2pd1k4WwWaY4RqipmcaDI61JEspqZ8eGbQnz999fw4pF+7rjvaQ7ETnpdloKViJfCoQCdJ0dp6TzldSlyCYbHJtjW0qWjVSIp5NZVC/jhPRs4NTLOHfc97fkqgIKViIfC9UFgss9KUt+Otm6Gx+KaXyWSYq5fUsmj995IsCSPf/hV1NNVgIs2r4vI7KkLFlFdmk9Taxe/F17idTlyEZFojDy/jw2JQCwiqWNJsIhHPr4R5yaXCb2iYCXiITMjHArQ1NKNc87TNwO5uEi0k3V1lRTl6a1TJBVVFOV5XYKWAkW8Fg4FONY/zOHuIa9LkQs41jfMK8cH2KT+KhG5AAUrEY+d6bPSvoEpLdKsMQsicnEKViIeW15dQmVRrvYNTHFbozFqSvO5Yn6p16WISApTsBLxmM9nNIYCnp8iLOc3EXc82dzJzQ3V6oMTkQtSsBJJAeFQkEPdgxztU59VKtrT3kvf0JjGLIjIRSlYiaSAs/cNlNQTiXZiBjc3qL9KRC5MwUokBVy5oIzSghz1WaWoSHOM1YvKCRR7fyq3iKQ2BSuRFOD3GevrAjozMAX1DY7x7KEejVkQkUuiYCWSIsKhAC2xU8QGUmOHdpn01IFO4g4FKxG5JApWIinidJ+Vzg5MLZFojNL8HK5bXOF1KSKSBhSsRFLEqkXlFOX52a7lwJThnCMSjbFxeZBcv94uReTi9E4hkiJy/T7WLq1UA3sKORA7yZG+YTavqPG6FBFJEwpWIikkHArw8rEBegdHvS5FgN+8MrmNjeZXicilUrASSSGn9w1Un1VqiDR3Ul9dTG1lkdeliEiaULASSSGra8vJz/FpOTAFDI9N0NTSxSYNBRWRy6BgJZJC8nP8XL+kQkesUsD21m5GxuNs1pgFEbkMClYiKaYxFGTfkT76h8e8LiWrRaIx8nJ8hOsDXpciImlEwUokxWwIBYg72HWwx+tSstrWaIzGugBFeTlelyIiaUTBSiTFXL+kkly/aUNmDx3pHaL5xEmdDSgil03BSiTFFOb5WV1boUGhHnqi+fSYBfVXicjlUbASSUHhUIA97X0Mjo57XUpWikQ7mVeWz8p5pV6XIiJpRsFKJAU1hgKMxx27D/Z6XUrWGZ+I8+T+TjY1VGNmXpcjImlGwUokBa2rC+AztBzogefb++gbGtMyoIhMi4KVSAoqyc9h1aJytmme1ZyLRGOYwU3L1bguIpdPwUokRYVDAYkFC8QAAB2ZSURBVJ473Mvw2ITXpWSVSHOM1bUVVBbneV2KiKQhBSuRFNUYCjI6Huf5w+qzmit9g2M8f7iXzQ06WiUi06NgJZKiGusCmGlD5rn05P5O4g42r1R/lYhMj4KVSIoqL8rlivll2pB5Dm2NnqC0IIdrayu8LkVE0pSClUgKC4cC7DrYw9hE3OtSMp5zjki0k5uWV5Hj11ujiEyP3j1EUlg4FGBobIIXOvq8LiXjNZ84ybH+YY1ZEJEZuWiwMrMHzOyEme0967qAmf3SzJoT3yvPuu0LZrbfzF4xs7fOVuEi2WB9KACgfQPnQCSqbWxEZOYu5YjV94BbX3Pd54HHnXMNwOOJy5jZVcB7gasTj7nPzPxJq1Yky1SV5LO8pkSDQufA1miM5TUlLKoo9LoUEUljFw1WzrkI8NqPy7cBDyZ+fhB411nX/9A5N+KcawX2A41JqlUkKzWGAuxs62Ei7rwuJWMNjU7Q1NrNpgYdrRKRmZluj9U859xRgMT3msT1i4DDZ92vPXHd65jZPWa208x2xmKxaZYhkvnCoQADI+O8dLTf61IyVlNrF6PjcTat0PwqEZmZZDevT7Vj6ZQfs51z9zvn1jnn1lVX61OiyPmEQ0EAtrVoOXC2RKKd5OX4zrzWIiLTNd1gddzMFgAkvp9IXN8OLD7rfrXAkemXJyLzywtYGizSPKtZFGmOEQ4FKMxTS6iIzMx0g9WPgbsTP98NPHbW9e81s3wzCwENwPaZlSgi4VCAHW3dxNVnlXQdvUPsP3GSzTobUESS4FLGLTwEPAOsNLN2M/sw8DfAm82sGXhz4jLOuX3Aj4AXgZ8Dn3DOaQdZkRlqDAXpHRwjemLA61IyjsYsiEgy5VzsDs65953nplvOc/+/Av5qJkWJyLnCiXlW21u7uWJ+mcfVZJZINMb8sgIaakq8LkVEMoAmr4ukgdrKQhaWF2hQaJKNT8R5cn8nm1ZUYTbVuTciIpdHwUokDZgZ4fogTa3dOKc+q2R5vr2XgeFxLQOKSNIoWImkiXAoQOfJEVo6T3ldSsbYGu3EZ3DTcs2vEpHkULASSRONZ/VZSXJsjca4dnEFFUV5XpciIhlCwUokTYSqiqkuzadJg0KToufUKHvae7WNjYgklYKVSJowMxpDAfVZJcmT+ztxTmMWRCS5FKxE0siGUICjfcO09wx5XUrai0RjlBXkcG1tudeliEgGUbASSSON2jcwKZxzRJpj3NxQTY5fb4Mikjx6RxFJIw01JVQW5aqBfYZeOT7A8f4RNq3Q2YAiklwKViJpxOd7tc9Kpk/b2IjIbFGwEkkzjaEgh7oHOdqnPqvpikQ7aagpYUF5odeliEiGUbASSTNhzbOakaHRCba3detolYjMCgUrkTRz5YIySgty2KZ9A6dlW2sXo+NxNitYicgsULASSTN+n7G+LsD2Vp0ZOB1bX4mRn+M7M8leRCSZFKxE0lBjKMCB2CliAyNel5J2Is0xwvVBCnL9XpciIhlIwUokDZ3us9rRpuXAy9HeM0hL7BSbGjRmQURmh4KVSBpataicojy/9g28TJFoJ4D6q0Rk1ihYiaShXL+PtUsrNc/qMkWiMRaWF7C8psTrUkQkQylYiaSpcCjAK8cH6B0c9bqUtDA2Eeep/Z1sWlGNmXldjohkKAUrkTTVGArinOZZXarnDvcyMDKu+VUiMqsUrETS1LWLy8nL8SlYXaJINIbP4MZlalwXkdmjYCWSpvJz/Fy/uEJ9VpcoEo1x3eIKyotyvS5FRDKYgpVIGgvXB9l3pI+B4TGvS0lp3adG2dPRx+YVNV6XIiIZTsFKJI2FQwHiDnYe7PG6lJT2RHMM52DTCi0DisjsUrASSWNrllSS6zf1WV1EJNpJRVEuq2srvC5FRDKcgpVIGivM87O6tkKDQi/AOccTzTFuXF6F36cxCyIyuxSsRNJcYyjAnvY+BkfHvS4lJb18bIATAyNsbtCYBRGZfQpWImkuHAowHnc8e6jX61JSUiQaA9D8KhGZEwpWImlu7dJKfIaWA89jazTGynmlzC8v8LoUEckCClYiaa60IJdVi8o1z2oKg6Pj7Gzr0dmAIjJnFKxEMkBjXYBnD/cyPDbhdSkpZVtLF6MTcS0DisicUbASyQDh+iCj43H2tPd5XUpKiUQ7Kcj1sb4u4HUpIpIlFKxEMkBjXQBTn9XrRKIxNtQHKcj1e12KiGQJBSuRDFBelMvKeaVsb1Of1WmHuwdp6TzFJo1ZEJE5pGAlkiE21AfZdbCHsYm416WkhK0asyAiHlCwEskQjaEAg6MTvNChPiuYXAZcVFHIsupir0sRkSwyo2BlZp82s71mts/MPpO47ktm1mFmzyW+3p6cUkXkQhpDkw3a2jcQxibiPH2gi00rqjDTNjYiMnemHazMbBXwEaARuBZ4h5k1JG7+inPuusTXz5JQp4hcRFVJPsuqi9XADjx7qJeTI+Ns1jKgiMyxmRyxuhLY5pwbdM6NA1uB25NTlohMR7g+yM62HibizutSPBWJxvD7jI3LNRhURObWTILVXmCTmQXNrAh4O7A4cdsnzWyPmT1gZpVTPdjM7jGznWa2MxaLzaAMETktHAowMDLOS0f7vS7FU1ujMa5fXEFZQa7XpYhIlpl2sHLOvQR8Gfgl8HPgeWAc+DqwDLgOOAr8/Xkef79zbp1zbl11tQ7XiyRDOBQEyOrtbbpOjrD3SJ/OBhQRT8yoed059x3n3Brn3CagG2h2zh13zk045+LAt5jswRKROTC/vIClwaKs7rN6cn8nzmnMgoh4Y6ZnBdYkvi8B7gAeMrMFZ93ldiaXDEVkjjTWBdje1k08S/ustkZjVBblcs2icq9LEZEsNNM5VlvM7EXgJ8AnnHM9wP8ysxfMbA/wRuCPZ1qkiFy6cH2Q3sExmk+c9LqUORePOyLRTm5qqMbv05gFEZl7OTN5sHPu5imu+8BMfqeIzEw4Mc+qqbWLlfNLPa5mbr10rJ/OkyNsatDZgCLiDU1eF8kwtZWFLCwvyMoG9ki0E1B/lYh4R8FKJMOYGeH6IE0t3TiXXX1WkWiMK+aXMq+swOtSRCRLKViJZKDGUIDOkyO0dp7yupQ5c2pknJ0HuzVtXUQ8pWAlkoFe7bPKnuXAbS1djE04LQOKiKcUrEQyUKiqmKqS/KyaZ7U1GqMw18+6uik3exARmRMKViIZaLLPKkBTa/b0WUWiMTbUB8jP8XtdiohkMQUrkQwVDgU42jdMe8+Q16XMukNdg7R1DWoZUEQ8p2AlkqGyad/Arc2TG7mrcV1EvKZgJZKhGmpKqCjKzYo+q0g0Rm1lIaGqYq9LEZEsp2AlkqF8Pjuzb2AmGx2P8/T+TjatqMZM29iIiLcUrEQyWLg+yMGuQY71DXtdyqzZfaiHU6MTbGrQMqCIeE/BSiSDnb1vYKaKRGP4fcbG5UGvSxERUbASyWRXLiijND8noxvYI80x1i6ppKwg1+tSREQUrEQymd9nrKurzNgG9s6TI+zt6GfTiiqvSxERARSsRDJeuD7IgdgpOk+OeF1K0j2RGLOg+VUikioUrEQyXGOiz2p7Bi4HRqKdBIrzWLWw3OtSREQABSuRjHfNonKK8vwZF6zicccTzTFuWl6Fz6cxCyKSGhSsRDJcrt/H2qWVbMuwPqsXj/bTeXJU09ZFJKUoWIlkgca6AK8cH6B3cNTrUpImkuivulmN6yKSQhSsRLJAuD6Ic7CjrcfrUpJm6ysxrlxQRk1pgdeliIicoWAlkgVW15aTl+PLmLELJ0fG2XWwR2MWRCTlKFiJZIGCXD/XL67ImH0DnznQxXjcsVnb2IhIilGwEskS4VCAvR19DAyPeV3KjEWiMYry/Kytq/S6FBGRcyhYiWSJcH2QuINdB9O/zyrSHOOG+iD5OX6vSxEROYeClUiWWLOkkhyfpf2+gW2dpzjYNahp6yKSkhSsRLJEYZ6f1bXlaT8oNKJtbEQkhSlYiWSRcH2QPe29DI1OeF3KtEWiMRYHCqkLFnldiojI6yhYiWSRxlCAsQnH7kPp2Wc1Oh7nmQNdbF5RjZm2sRGR1KNgJZJF1i2txGekbZ/VroM9nBqdYJPGLIhIilKwEskipQW5XL2wPG0HhW6NxsjxGTcsC3pdiojIlBSsRLJMOBTg2cO9jIynX59VJBpjzdJKSgtyvS5FRGRKClYiWSZcH2R0PM7zh/u8LuWyxAZGePFoP5t1NqCIpDAFK5Ess76uEjPY3ppey4FPJMYsKFiJSCpTsBLJMhVFeaycV5p2DeyRaIxgcR5XLSjzuhQRkfNSsBLJQuFQgF0HexibiHtdyiWJxx2R5k5ubqjC59OYBRFJXQpWIlkoXB9kcHSCvR3p0We170g/3adGNW1dRFLejIKVmX3azPaa2T4z+0ziuoCZ/dLMmhPftf28SIpZXxcA0mee1eltbG7W/CoRSXHTDlZmtgr4CNAIXAu8w8wagM8DjzvnGoDHE5dFJIVUl+azrLo4bfYN3BqNcfXCMqpL870uRUTkgmZyxOpKYJtzbtA5Nw5sBW4HbgMeTNznQeBdMytRRGZDYyjIjtZuJuLO61IuaGB4jN0He7QMKCJpYSbBai+wycyCZlYEvB1YDMxzzh0FSHyvmerBZnaPme00s52xWGwGZYjIdGyoDzAwMs5LR/u9LuWCnj7QxXjcaRsbEUkL0w5WzrmXgC8DvwR+DjwPjF/G4+93zq1zzq2rrtYbpshcawylR59VJBqjOM/P2qVq1xSR1Dej5nXn3Hecc2ucc5uAbqAZOG5mCwAS30/MvEwRSbYF5YUsCRSl9L6BzjkizTFuWBYkL0cnMYtI6pvpWYE1ie9LgDuAh4AfA3cn7nI38NhMnkNEZk84FGBHWzfxFO2zausa5HD3kKati0jamOlHwC1m9iLwE+ATzrke4G+AN5tZM/DmxGURSUGNoQA9g2M0nzjpdSlTikQn+y/VuC4i6SJnJg92zt08xXVdwC0z+b0iMjc21AeByX0DV84v9bia19sajbE0WMTSYLHXpYiIXBI1LYhksdrKQhaUF7AtBRvYR8YneOZAl84GFJG0omAlksXMjHAowPbWbpxLrT6rXW09DI1NaBlQRNKKgpVIlgvXB4kNjNDaecrrUs6xtTlGrt+4YVnQ61JERC6ZgpVIljs9zyrVtreJRDtZu7SSkvwZtYKKiMwpBSuRLFdfVUxVSX5KDQo90T/MS0f7tQwoImlHwUoky53us2pq6UqZPqtIcyeAGtdFJO0oWIkI4foAR/qGae8Z8roUYHJ+VVVJHlctKPO6FBGRy6JgJSIptW9gPO54cn8nmxqq8fnM63JERC6LgpWIsKKmlIqiXLa3er9v4N4jfXSfGlV/lYikJQUrEcHnMxrrAilxxGrrK5Pb2NzUUOVxJSIil0/BSkSAyeXAg12DHOsb9rSOSHOMVYvKqCrJ97QOEZHpULASEeDVfQObPFwO7B8eY/ehXp0NKCJpS8FKRAC4ckEZpfk5ni4HPr2/i4m4Y7P6q0QkTSlYiQgAfp+xrq7S0wnskeYYJfk5rFla6VkNIiIzoWAlImc0hoLsP3GSzpMjc/7czjki0Rg3LAuS69dbk4ikJ717icgZ4frJeVY7PDhq1dJ5ivaeIY1ZEJG0pmAlImdcs6icwly/J31WkejkmIXNalwXkTSmYCUiZ+T6faxdWulZsApVFbMkWDTnzy0ikiwKViJyjnAowMvH+ukbHJuz5xwZn2BbSzebNBRURNKcgpWInKMxFMA52N42d0etdrb1MDQ2of4qEUl7ClYico5rF1eQl+Ob030Dt0Zj5PrtzJBSEZF0pWAlIucoyPVz3eKKOe2zikRjrFsaoDg/Z86eU0RkNihYicjrbAgF2NvRx8mR8Vl/ruP9w7x8bIDNK7UMKCLpT8FKRF6nMRQk7mDnHPRZnR6zoP0BRSQTKFiJyOusWVpBjs/mZHubSHMn1aX5XLmgdNafS0RktilYicjrFOXlsLq2fNb7rCbijieaY9zcUIWZzepziYjMBQUrEZlSYyjInvZehkYnZu05Xujoo3dwjM0asyAiGULBSkSmFK4PMDbhePZQz6w9RyQawwxuWq7BoCKSGRSsRGRK65ZW4jPYNovLgZFojGsWlRMsyZ+15xARmUsKViIypdKCXK5eWD5rg0L7hsZ49nCvzgYUkYyiYCUi59UYCvDsoV5GxpPfZ/X0/k4m4k7b2IhIRlGwEpHzCocCjIzH2dPel/TfHWmOUZKfw/VLKpL+u0VEvKJgJSLn1RgKANDUktzlQOcckWgnNy4PkuvX25CIZA69o4nIeVUU5XHF/NKkz7M6EDtFR++QlgFFJOMoWInIBYVDAXYd7GFsIp6036ltbEQkUylYicgFNYaCDI5OsLcjeX1WW6Mx6quKWRwoStrvFBFJBTMKVmb2x2a2z8z2mtlDZlZgZl8ysw4zey7x9fZkFSsic+90n1Wy9g0cHpugqbVLy4AikpGmHazMbBHwKWCdc24V4Afem7j5K8656xJfP0tCnSLikerSfOqri5PWZ7WjrZvhsbi2sRGRjDTTpcAcoNDMcoAi4MjMSxKRVBMOBdnR1s1E3M34d0WiMfL8PsL1gSRUJiKSWqYdrJxzHcDfAYeAo0Cfc+4/Ezd/0sz2mNkDZlY51ePN7B4z22lmO2Ox2HTLEJE5sKE+wMDwOC8d7Z/x74pEO1kfqqQoLycJlYmIpJaZLAVWArcBIWAhUGxm7we+DiwDrmMycP39VI93zt3vnFvnnFtXXa0lAZFUlqw+q6N9Q7xyfEBnA4pIxprJUuCbgFbnXMw5NwY8Amx0zh13zk045+LAt4DGZBQqIt5ZUF7IkkARTTPcN/CJaCeAGtdFJGPNJFgdAjaYWZGZGXAL8JKZLTjrPrcDe2dSoIikhsZQgO2t3cRn0Ge1tTlGTWk+V8wvTWJlIiKpYyY9Vk3Aw8Bu4IXE77of+F9m9oKZ7QHeCPxxMgoVEW+FQwF6BsfYHzs5rcdPxB1PNneyaUU1k5/FREQyz4y6R51zXwS++JqrPzCT3ykiqSkcCgKT+waumHf5R5z2tPfSNzSmZUARyWiavC4il2RxoJAF5QXTnme1NRrDDG5eXpXkykREUoeClYhcEjMjHArQ1NqNc5ffZxWJxli9qJzK4rxZqE5EJDUoWInIJWsMBYkNjNDWNXhZj+sbHOO5w72ati4iGU/BSkQu2elp6U0tlzd24akDncSdxiyISOZTsBKRS1ZfVUxVSf5l91lFojFKC3K4bnHFLFUmIpIaFKxE5JKd7rO6nAnszjm2RmPcuKyKHL/eckQks+ldTkQuS2MoQEfvEIe7L63Pav+JkxztG9YyoIhkBQUrEbksp/usLvWo1dbo5Cbrm1ZozIKIZD4FKxG5LCtqSqkoyr3kfQMjzZ0sqy6mtrJolisTEfGegpWIXBafz1hfd2l9VsNjEzS1dGkZUESyhoKViFy2cChAW9cgx/uHL3i/ptZuRsbjClYikjUUrETksp3eN3DbReZZRaIx8nJ8bEjcX0Qk0ylYichlu2phGSX5ORddDoxEY4RDAQrz/HNUmYiItxSsROSy+X3GurrKCw4KPdI7RPOJk2xq0DKgiGQPBSsRmZZwKMj+EyfpPDky5e1PNJ8es6BgJSLZQ8FKRKalMTQ5z2rHeY5abY3GmF9WwIp5JXNZloiIpxSsRGRaVteWU5jrn3I5cHwizpPNndzcUIWZeVCdiIg3FKxEZFpy/T7WLp26z+r59j76h8fZvFLLgCKSXRSsRGTaGkMBXj7WT9/g2DnXR6IxfAY3Ldc2NiKSXRSsRGTawqEAzsGOtnOPWkWaY6yuraCiKM+jykREvKFgJSLTdu3iCvJyfOfsG9g7OMrzh3t1NqCIZCUFKxGZtoJcP9ctrjhnUOiT+zuJO9i8QsuAIpJ9FKxEZEbCoQB7j/RzcmQcmOyvKivI4draCo8rExGZewpWIjIj4VCQibhj18EenHNEop3c1FBFjl9vLyKSfXK8LkBE0tuapRXk+Iymli4WlBdwrH9Y29iISNZSsBKRGSnKy+Ga2nK2t3ZTmTgLUI3rIpKtFKxEZMbCoSDfebIFM1heU8LCikKvSxIR8YSaIERkxsKhAGMTjh1tPWzW0SoRyWIKViIyY2vrKvEltgTUMqCIZDMFKxGZsbKCXK5aWEZ+jo9wKOB1OSIinlGPlYgkxSffuJz2niEKcv1elyIi4hkFKxFJiltXLfC6BBERz2kpUERERCRJFKxEREREkkTBSkRERCRJFKxEREREkmRGwcrM/tjM9pnZXjN7yMwKzCxgZr80s+bE98pkFSsiIiKSyqYdrMxsEfApYJ1zbhXgB94LfB543DnXADyeuCwiIiKS8Wa6FJgDFJpZDlAEHAFuAx5M3P4g8K4ZPoeIiIhIWph2sHLOdQB/BxwCjgJ9zrn/BOY5544m7nMUqJnq8WZ2j5ntNLOdsVhsumWIiIiIpIyZLAVWMnl0KgQsBIrN7P2X+njn3P3OuXXOuXXV1dpbTERERNLfTJYC3wS0Oudizrkx4BFgI3DczBYAJL6fmHmZIiIiIqlvJsHqELDBzIrMzIBbgJeAHwN3J+5zN/DYzEoUERERSQ/T3ivQOddkZg8Du4Fx4FngfqAE+JGZfZjJ8HVXMgoVERERSXUz2oTZOfdF4IuvuXqEyaNXIiIiIlnFnHNe14CZxYCDc/BUVUDnHDxPutLrc3F6jS5Mr8/F6TW6ML0+F6bX5+Lm4jVa6pyb8sy7lAhWc8XMdjrn1nldR6rS63Nxeo0uTK/Pxek1ujC9Phem1+fivH6NtFegiIiISJIoWImIiIgkSbYFq/u9LiDF6fW5OL1GF6bX5+L0Gl2YXp8L0+tzcZ6+RlnVYyUiIiIym7LtiJWIiIjIrMmKYGVmt5rZK2a238w+73U9qcbMHjCzE2a21+taUpGZLTaz/zKzl8xsn5l92uuaUo2ZFZjZdjN7PvEa/ZnXNaUiM/Ob2bNm9u9e15KKzKzNzF4ws+fMbKfX9aQaM6sws4fN7OXE+9ENXteUSsxsZeLvzumvfjP7zJzXkelLgWbmB6LAm4F2YAfwPufci54WlkLMbBNwEvi+c26V1/WkmsSelwucc7vNrBTYBbxLf4deldjWqtg5d9LMcoEngU8757Z5XFpKMbPPAuuAMufcO7yuJ9WYWRuwzjmnOU1TMLMHgSecc982szygyDnX63VdqSjxb38HEHbOzcWczDOy4YhVI7DfOdfinBsFfgjc5nFNKcU5FwG6va4jVTnnjjrndid+HmByT8xF3laVWtykk4mLuYmvzP7UdpnMrBb4beDbXtci6cfMyoBNwHcAnHOjClUXdAtwYK5DFWRHsFoEHD7rcjv6R1GmyczqgOuBJm8rST2JZa7ngBPAL51zeo3O9Q/A54C414WkMAf8p5ntMrN7vC4mxdQDMeC7ieXkb5tZsddFpbD3Ag958cTZEKxsiuv0SVoum5mVAFuAzzjn+r2uJ9U45yacc9cBtUCjmWlZOcHM3gGccM7t8rqWFHejc24N8DbgE4k2BZmUA6wBvu6cux44BahneAqJZdJ3Av/Xi+fPhmDVDiw+63ItcMSjWiRNJfqGtgD/4px7xOt6UllieeI3wK0el5JKbgTemegh+iHwW2b2z96WlHqcc0cS308AjzLZyiGT2oH2s44EP8xk0JLXexuw2zl33Isnz4ZgtQNoMLNQIsW+F/ixxzVJGkk0Zn8HeMk597+9ricVmVm1mVUkfi4E3gS87G1VqcM59wXnXK1zro7J96BfO+fe73FZKcXMihMnh5BY4noLoDOVE5xzx4DDZrYycdUtgE6gmdr78GgZECYPLWY059y4mX0S+AXgBx5wzu3zuKyUYmYPAW8AqsysHfiic+473laVUm4EPgC8kOghAvgfzrmfeVhTqlkAPJg4E8cH/Mg5p5ECcjnmAY9Ofo4hB/iBc+7n3paUcv4b8C+JgwQtwIc8riflmFkRk1MAPupZDZk+bkFERERkrmTDUqCIiIjInFCwEhEREUkSBSsRERGRJFGwEhEREUkSBSsRERGRJFGwEhEREUkSBSsRERGRJFGwEhEREUmS/x9ekXLwdgRjBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "curso.IQ.plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Índice de Masa Corporal\n",
    "\n",
    "## IMC = peso/estatura al cuadrado\n",
    "\n",
    "# Bajo de Peso -> Igual o menor a 18.4\n",
    "#       Normal -> 18.5 a 24.9\n",
    "#    Sobrepeso -> 25 a 29.9\n",
    "#     Obesidad -> Mayor o igual a 30\n",
    "\n",
    "##### Usando todo lo aprendido\n",
    "\n",
    "np.random.seed(2020)\n",
    "\n",
    "nombre = ['Javier', 'Olivia', 'Flora', 'Gabriel', 'Luis', 'Josué', 'Francisco', 'Mayra']\n",
    "peso = np.random.randint(60, 150, size=(8))\n",
    "estatura = np.random.uniform(1.5, 1.8, 8).round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMC = pd.DataFrame()\n",
    "IMC['Nombre'] = nombre\n",
    "IMC['Peso_Kg'] = peso\n",
    "IMC['Estatura_Cm'] = estatura"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMC['IMC'] = IMC['Peso_Kg'] / (IMC['Estatura_Cm'] ** 2)\n",
    "\n",
    "# A la tradicional\n",
    "#IMC['IMC'] = round(IMC['Peso_Kg'] / (IMC['Estatura_Cm']  ** 2), 1)\n",
    "\n",
    "# A la Pandas \n",
    "#IMC['IMC'] = (IMC['Peso_Kg'] / (IMC['Estatura_Cm']  ** 2)).round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Peso_Kg</th>\n",
       "      <th>Estatura_Cm</th>\n",
       "      <th>IMC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Javier</td>\n",
       "      <td>68</td>\n",
       "      <td>1.6</td>\n",
       "      <td>26.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Olivia</td>\n",
       "      <td>127</td>\n",
       "      <td>1.6</td>\n",
       "      <td>49.609375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flora</td>\n",
       "      <td>127</td>\n",
       "      <td>1.8</td>\n",
       "      <td>39.197531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gabriel</td>\n",
       "      <td>63</td>\n",
       "      <td>1.5</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Luis</td>\n",
       "      <td>131</td>\n",
       "      <td>1.5</td>\n",
       "      <td>58.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Josué</td>\n",
       "      <td>116</td>\n",
       "      <td>1.7</td>\n",
       "      <td>40.138408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Francisco</td>\n",
       "      <td>89</td>\n",
       "      <td>1.7</td>\n",
       "      <td>30.795848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mayra</td>\n",
       "      <td>108</td>\n",
       "      <td>1.6</td>\n",
       "      <td>42.187500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nombre  Peso_Kg  Estatura_Cm        IMC\n",
       "0     Javier       68          1.6  26.562500\n",
       "1     Olivia      127          1.6  49.609375\n",
       "2      Flora      127          1.8  39.197531\n",
       "3    Gabriel       63          1.5  28.000000\n",
       "4       Luis      131          1.5  58.222222\n",
       "5      Josué      116          1.7  40.138408\n",
       "6  Francisco       89          1.7  30.795848\n",
       "7      Mayra      108          1.6  42.187500"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMC['Cat_IMC'] = IMC[['IMC']].applymap(lambda x: 'Normal' if (x < 25) else 'SobrePeso' if ((x > 25) & (x < 30)) else 'Obesidad' if (x > 30) else 'Sabrocito')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Peso_Kg</th>\n",
       "      <th>Estatura_Cm</th>\n",
       "      <th>IMC</th>\n",
       "      <th>Cat_IMC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Javier</td>\n",
       "      <td>68</td>\n",
       "      <td>1.6</td>\n",
       "      <td>26.562500</td>\n",
       "      <td>SobrePeso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Olivia</td>\n",
       "      <td>127</td>\n",
       "      <td>1.6</td>\n",
       "      <td>49.609375</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Flora</td>\n",
       "      <td>127</td>\n",
       "      <td>1.8</td>\n",
       "      <td>39.197531</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gabriel</td>\n",
       "      <td>63</td>\n",
       "      <td>1.5</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>SobrePeso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Luis</td>\n",
       "      <td>131</td>\n",
       "      <td>1.5</td>\n",
       "      <td>58.222222</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Josué</td>\n",
       "      <td>116</td>\n",
       "      <td>1.7</td>\n",
       "      <td>40.138408</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Francisco</td>\n",
       "      <td>89</td>\n",
       "      <td>1.7</td>\n",
       "      <td>30.795848</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mayra</td>\n",
       "      <td>108</td>\n",
       "      <td>1.6</td>\n",
       "      <td>42.187500</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Nombre  Peso_Kg  Estatura_Cm        IMC    Cat_IMC\n",
       "0     Javier       68          1.6  26.562500  SobrePeso\n",
       "1     Olivia      127          1.6  49.609375   Obesidad\n",
       "2      Flora      127          1.8  39.197531   Obesidad\n",
       "3    Gabriel       63          1.5  28.000000  SobrePeso\n",
       "4       Luis      131          1.5  58.222222   Obesidad\n",
       "5      Josué      116          1.7  40.138408   Obesidad\n",
       "6  Francisco       89          1.7  30.795848   Obesidad\n",
       "7      Mayra      108          1.6  42.187500   Obesidad"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Índice de Masa Corporal\n",
    "\n",
    "## IMC = peso/estatura al cuadrado\n",
    "\n",
    "# Bajo de Peso -> Igual o menor a 18.4\n",
    "#       Normal -> 18.5 a 24.9\n",
    "#    Sobrepeso -> 25 a 29.9\n",
    "#     Obesidad -> Mayor o igual a 30\n",
    "##### Usando todo lo aprendido\n",
    "\n",
    "##### TIENES 5 MINUTOS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se necesita el ÍNDICE de MASA CORPORAL de todos estos sujetos.\n",
    "############# Los PESOS son aleatorios.\n",
    "############# Las ESTATURAS son aleatorias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "nombres = \"\"\"\n",
    "['Álan', 'Jacinto', 'Martinez', 'Alicia', 'Jesús', 'Mirta', 'Andrea', 'Josefina', 'Mónica', 'Andrés', 'Juan', 'Nicolás', 'Antonia', 'Juana', 'Noé', 'Antonio', \n",
    " 'Juano', 'Noelia', 'Azul', 'Julia', 'Paula', 'Bartolomé', 'Julián', 'Pomponio', 'Belén', 'Juliana', 'Renzo', 'Celeste', 'Julio', 'Rodrigo', 'Edgardo', 'Leandra',\n",
    " 'Rodriguez', 'Felicia', 'Luis', 'Romina', 'Florencia', 'Luisa', 'Rosario', 'Gaspar', 'Marcelo', 'Tato', 'Gerardo', 'Marcos', 'Tomás', 'Gimenez', 'María',\n",
    " 'Victor', 'Gonzalo', 'Mariano', 'Yayo', 'Gustavo', 'Martín', 'Zulema', 'Abril', 'Alonso', 'Amparo', 'Ángel', 'Ángeles', 'Angustias', 'Aurora', 'Belén',\n",
    " 'Benjamín', 'Buenaventura', 'Candela', 'Chile', 'Comillas', 'Concha', 'Delfín', 'Dolores', 'Domingo', 'Encarnación', 'Esmeralda', 'Esperanza', 'Estela',\n",
    " 'Felicidad', 'Felipe', 'Flora', 'Gema', 'Gloria', 'Granada', 'Jacinto', 'Julio', 'Lázaro', 'León', 'Leticia', 'Lidia', 'Lima', 'Luz', 'Magdalena', 'Margarita',\n",
    " 'Mateo', 'Melisa', 'Milagros', 'Narciso', 'Nieves', 'Óscar', 'Paloma', 'Patricio', 'Paz', 'Pedro', 'Piedad', 'Potes', 'Prudencia', 'Purificación', 'Rebeca', \n",
    " 'Remedios', 'Rosa', 'Rosario', 'Rubí', 'Soledad', 'Tecla', 'Tierra', 'Valeriana', 'Verónica', 'Victoria', 'Violeta']\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "nombres = eval(nombres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "peso = np.random.randint(60, 150, len(nombres))\n",
    "estatura = np.random.uniform(1.2, 2, len(nombres)).round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMC = pd.DataFrame()\n",
    "IMC['Nombre'] = nombres\n",
    "IMC['Peso_Kg'] = peso\n",
    "IMC['Estatura_Cm'] = estatura\n",
    "IMC['IMC'] = IMC['Peso_Kg'] / (IMC['Estatura_Cm'] ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMC['Cat_IMC'] = IMC[['IMC']].applymap(lambda x: 'Normal' if (x < 25) else 'SobrePeso' if ((x > 25) & (x < 30)) else 'Obesidad' if (x > 30) else 'Sabrocito')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nombre</th>\n",
       "      <th>Peso_Kg</th>\n",
       "      <th>Estatura_Cm</th>\n",
       "      <th>IMC</th>\n",
       "      <th>Cat_IMC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Álan</td>\n",
       "      <td>68</td>\n",
       "      <td>1.4</td>\n",
       "      <td>34.693878</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jacinto</td>\n",
       "      <td>127</td>\n",
       "      <td>1.7</td>\n",
       "      <td>43.944637</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Martinez</td>\n",
       "      <td>127</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.750000</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alicia</td>\n",
       "      <td>63</td>\n",
       "      <td>1.9</td>\n",
       "      <td>17.451524</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jesús</td>\n",
       "      <td>131</td>\n",
       "      <td>1.3</td>\n",
       "      <td>77.514793</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Tierra</td>\n",
       "      <td>134</td>\n",
       "      <td>1.8</td>\n",
       "      <td>41.358025</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Valeriana</td>\n",
       "      <td>144</td>\n",
       "      <td>2.0</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Verónica</td>\n",
       "      <td>92</td>\n",
       "      <td>1.7</td>\n",
       "      <td>31.833910</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Victoria</td>\n",
       "      <td>69</td>\n",
       "      <td>1.3</td>\n",
       "      <td>40.828402</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Violeta</td>\n",
       "      <td>116</td>\n",
       "      <td>1.3</td>\n",
       "      <td>68.639053</td>\n",
       "      <td>Obesidad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Nombre  Peso_Kg  Estatura_Cm        IMC   Cat_IMC\n",
       "0         Álan       68          1.4  34.693878  Obesidad\n",
       "1      Jacinto      127          1.7  43.944637  Obesidad\n",
       "2     Martinez      127          2.0  31.750000  Obesidad\n",
       "3       Alicia       63          1.9  17.451524    Normal\n",
       "4        Jesús      131          1.3  77.514793  Obesidad\n",
       "..         ...      ...          ...        ...       ...\n",
       "112     Tierra      134          1.8  41.358025  Obesidad\n",
       "113  Valeriana      144          2.0  36.000000  Obesidad\n",
       "114   Verónica       92          1.7  31.833910  Obesidad\n",
       "115   Victoria       69          1.3  40.828402  Obesidad\n",
       "116    Violeta      116          1.3  68.639053  Obesidad\n",
       "\n",
       "[117 rows x 5 columns]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base de datos reales\n",
    "[pandas - Python Data Analysis Library](https://pandas.pydata.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Base\n",
    "file_csv = 'Curso Python para Psicólogos.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cuestionario = pd.read_csv(file_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cuestionario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Localización del lugar correspondiente de las columnas\n",
    "for i, j in enumerate(cuestionario.columns):\n",
    "    print(i, j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cuestionario[cuestionario.columns[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arreglo de la 1ra Columna\n",
    "cuestionario[cuestionario.columns[0]] = cuestionario[cuestionario.columns[0]].str.strip('GMT-{}'.format(x for x in range(100)))\n",
    "cuestionario[cuestionario.columns[0]] = pd.to_datetime(cuestionario[cuestionario.columns[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cuestionario[cuestionario.columns[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arreglo de la 2da Columna\n",
    "cuestionario[cuestionario.columns[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arreglo de la 3ra Columna\n",
    "print(cuestionario[cuestionario.columns[2]])\n",
    "cuestionario[cuestionario.columns[2]] = pd.to_datetime(cuestionario[cuestionario.columns[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creando una columna con las diferencias de tiempo\n",
    "print(cuestionario.columns[0])\n",
    "print(cuestionario[cuestionario.columns[0]])\n",
    "print(' ')\n",
    "print(cuestionario.columns[2])\n",
    "print(cuestionario[cuestionario.columns[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cuestionario['Diferencia'] = (cuestionario[cuestionario.columns[0]] - cuestionario[cuestionario.columns[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cuestionario['Diferencia']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arreglo de la 4ta Columna\n",
    "print(cuestionario[cuestionario.columns[3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arreglo de la 5ta Columna\n",
    "print(cuestionario[cuestionario.columns[4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cuestionario[cuestionario.columns[4]] = cuestionario[cuestionario.columns[4]].str.strip('años').str.strip().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cuestionario[cuestionario.columns[4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arreglo de la 6ta Columna\n",
    "print(cuestionario[cuestionario.columns[5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "info = {'FaceBook':1, 'Instagram':2, 'Twitter':3, 'Otros':4}\n",
    "cuestionario[cuestionario.columns[5]] = cuestionario[cuestionario.columns[5]].replace(info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cuestionario[cuestionario.columns[5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arreglo de la 7ma Columna\n",
    "print(cuestionario[cuestionario.columns[6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nivel_curso = {'Curso Introducción a Python para Psicólogos':1, 'Curso Intermedio a Python para Psicólogos':2, 'Ambos':3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cuestionario[cuestionario.columns[6]] = cuestionario[cuestionario.columns[6]].replace(nivel_curso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cuestionario[cuestionario.columns[6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arreglo de la 8va Columna\n",
    "print(cuestionario[cuestionario.columns[7]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arreglo de la 9na Columna\n",
    "print(cuestionario[cuestionario.columns[8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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