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Commit f3897807 authored by Carlos David García Hernández's avatar Carlos David García Hernández
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.ipynb_checkpoints/02-Comprehension&Documentation-checkpoint.ipynb
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......@@ -5,6 +5,7 @@
"metadata": {},
"source": [
"# 2. Python\n",
<<<<<<< HEAD
"## 2.1 Comprensiónes"
]
},
......@@ -13,13 +14,685 @@
"metadata": {},
"source": [
"## 2.2 Generadores"
=======
"## 2.1 Comprensiónes\n",
"Las comprensiones de python proveen de una forma consisa de crear listas, diccionarios y conjuntos. Su nombre proviene de teria de conjuntos en donde la *notación contructiva de conjuntos* o comprensión se define como:\n",
"\n",
"\n",
"[Wikipedia](https://en.wikipedia.org/wiki/Set-builder_notation): Definir conjuntos por propiedades también se conoce como ***comprensión de conjuntos***, abstracción de conjuntos o como definición de la intención de un conjunto.\n",
"\n",
"En python la estructura de una comprensión es la siguiente:![img](https://python-3-patterns-idioms-test.readthedocs.io/en/latest/_images/listComprehensions.gif)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2, 2, 4, 6, 8]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"old_list = [1,2,2,3,4,5,6,7,7,8,9]\n",
"new_list = []\n",
"for i in old_list:\n",
" if i%2==0:\n",
" new_list.append(i)\n",
"new_list"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2, 2, 4, 6, 8]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Lista\n",
"new_list=[i for i in old_list if i%2==0]\n",
"new_list"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: 1,\n",
" 1: 1,\n",
" 2: 4,\n",
" 3: 27,\n",
" 4: 256,\n",
" 5: 3125,\n",
" 6: 46656,\n",
" 7: 823543,\n",
" 8: 16777216,\n",
" 9: 387420489}"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Diccionario\n",
"{ i:i**i for i in range(10)}"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[1, 0, 0], [0, 1, 0], [0, 0, 1]]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Comprensión anidada\n",
"[ [ 1 if item_idx == row_idx else 0 for item_idx in range(0, 3) ] for row_idx in range(0, 3) ]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{1, 2, 3, 4, 5, 6, 7, 8, 9}"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Conjunto\n",
"new_set={i for i in old_list}\n",
"new_set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.2 Expresiones lambda $\\lambda$\n",
"Una función anónima o **expresión lambda** es una subrutina definida que no está enlazada a un identificador. Las funciones lambda generalmente son Argumentos que son pasados a otras funciónes de orden superior o Usadas para construir el resultado de una función de orden superior que necesita retornar una función [[Wikiedia](https://en.wikipedia.org/wiki/Anonymous_function)].\n",
"\n",
"En Python las expresiones lambda no pueden utilizar ciclos ni utilizar la plabra reservada **return**, su sintaxis es:```lambda <parametros>:<expresion>```\n",
"\n",
"*Nota: **filter** regresa una lista de elementos para los cuales una funcion regresa **True**; **map** aplica una funcion a todos los ementos de una lista.*\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"function"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = lambda x: x * x\n",
"type(f)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[4, 4, 256, 46656, 16777216]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(map(lambda i: i**i, filter(lambda i: i%2==0, old_list)))"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(13, -3), (4, 1), (1, 2), (9, 10)]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = [(1, 2), (4, 1), (9, 10), (13, -3)]\n",
"a.sort(key=lambda x: x[1])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[102.56, 97.7, 99.14, 100.03999999999999]\n"
]
}
],
"source": [
"Celsius = [39.2, 36.5, 37.3, 37.8]\n",
"Fahrenheit = map(lambda x: (float(9)/5)*x + 32, Celsius)\n",
"print(list(Fahrenheit))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.3 Generadores: Funciones y Expresiones\n",
"Los Generadores son funciones o expresiones que regesan un valor iterador en lugar de un valor, las para que una funcion regrese un generador en lugar de un valor se utiliza la palabra reservada **yield** en lugar de **return**."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"499999500000\n"
]
}
],
"source": [
"def firstn(n):\n",
" num = 0\n",
" while num < n:\n",
" yield num\n",
" num += 1\n",
"\n",
"sum_of_first_n = sum(firstn(1000000))\n",
"print(sum_of_first_n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tambien es posible construir generadores utilizando expresiones utilizando parentesis en lugar de corchetes. Esto resulta util cuando utilizar una expresion generaria una lista muy grande que ocuparia mucha memoria."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n",
"0\n",
"2\n",
"4\n",
"[6, 8, 10, 12, 14, 16, 18]\n"
]
}
],
"source": [
"doublesC = [2 * n for n in range(10)]\n",
"print(doublesC)\n",
"doublesG = (2 * n for n in range(10))\n",
"print(next(doublesG))\n",
"print(next(doublesG))\n",
"print(next(doublesG))\n",
"print(list(doublesG))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.4 Ejercicios\n",
"### 2.4.1\n",
"Dada una lista de enteros **a**, utilisando una comprensión, Imprimir una nueva lista que contenga \"par\" o \"impar\" en cada uno de sus elementos, dependiendo del valor del elemento en la lista orginal."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4.2\n",
"Dada una lista *a*, utilizando una comprensión, imprimir una lista de tuplas, en donde cada tupla contiene el indice y el valor de elemento de la lista orginal."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"my_list = ['apple', 'banana', 'grapes', 'pear']\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4.3\n",
"Genera una lista que contenga una tuplas con todos los pares posibles de elementos entre las dos listas."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"a = [0,1,2,3,4,5,6]\n",
"b = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4.4 \n",
"Utilizando *filter* y una expresion *lambda* generar una lista que contenga todos los numeros impares de una lista de entrada **a**."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"a=[5, 7, 22, 97, 54, 62, 77, 23, 73, 61]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4.5\n",
"Utilizado ***reduce*** y una expresion *lambda*, obten la suma de todos lo elementos en una lista."
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"a = [5, 8, 10, 20, 50, 100] "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4.6 \n",
"Utilizando *map* y una expresion *lambda* obtener una lista cuyos elementos sean la suma de los elementos correspondientes en las listas **a** y **b**.\n"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"a = [5, 10, 15, 20]\n",
" \n",
"b = [30, 35, 40, 45]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.6.7\n",
"Escribir un generador en forma de expresion que obtenga las primeras 10 ternas pitagoricas.\n",
"\n",
"[Wikipedia](https://es.wikipedia.org/wiki/Terna_pitag%C3%B3rica): Una terna pitagórica es un conjunto ordenado de tres números enteros positivos a, b, c, y son solución de la ecuación diofantina cuadrática $a^{2}+b^{2}=c^{2}$.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.6.8 \n",
"Escribir un generador en forma de funcion recursiva que entregue la lista de todas las permitaciones de los elementos en una lista."
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"a = [\"a\", \"b\", \"c\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.5 Modulos y Paquetes\n",
"En Python, cada uno de nuestros archivos .py se denominan módulos. Estos módulos, a la vez, pueden formar parte de paquetes. Un paquete, es una carpeta que contiene archivos .py. Para que una carpeta pueda ser considerada un paquete, debe contener un archivo de inicio llamado ``__init__.py``. Este archivo, no necesita contener ninguna instrucción. De hecho, puede estar vacío.\n",
"\n",
"\n",
"``\n",
"└── miModulo\n",
" ├── __init__.py \n",
" └── helloWOrld.py \n",
"``"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n"
]
}
],
"source": [
"from miModulo import helloWorld\n",
"helloWorld.hello()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.6 Documentacion (docstring)\n",
"\n",
"Python **Docstring** es el texto de documentación que puede aprecer en la definición de una clase, módulo, función o método, y se escribe como la primera declaración. Se puede acceder a las cadenas de documenacion desde el atributo doc para cualquiera de los objetos de Python y también con la función incorporada **help()**."
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generators have a ``Yields`` section instead of a ``Returns`` section.\n",
"\n",
" Args:\n",
" n (int): The upper limit of the range to generate, from 0 to `n` - 1.\n",
"\n",
" Yields:\n",
" int: The next number in the range of 0 to `n` - 1.\n",
"\n",
" Examples:\n",
" Examples should be written in doctest format, and should illustrate how\n",
" to use the function.\n",
"\n",
" >>> print([i for i in example_generator(4)])\n",
" [0, 1, 2, 3]\n",
"\n",
" \n",
"Help on function example_generator in module __main__:\n",
"\n",
"example_generator(n)\n",
" Generators have a ``Yields`` section instead of a ``Returns`` section.\n",
" \n",
" Args:\n",
" n (int): The upper limit of the range to generate, from 0 to `n` - 1.\n",
" \n",
" Yields:\n",
" int: The next number in the range of 0 to `n` - 1.\n",
" \n",
" Examples:\n",
" Examples should be written in doctest format, and should illustrate how\n",
" to use the function.\n",
" \n",
" >>> print([i for i in example_generator(4)])\n",
" [0, 1, 2, 3]\n",
"\n"
]
}
],
"source": [
"def example_generator(n):\n",
" \"\"\"Generators have a ``Yields`` section instead of a ``Returns`` section.\n",
"\n",
" Args:\n",
" n (int): The upper limit of the range to generate, from 0 to `n` - 1.\n",
"\n",
" Yields:\n",
" int: The next number in the range of 0 to `n` - 1.\n",
"\n",
" Examples:\n",
" Examples should be written in doctest format, and should illustrate how\n",
" to use the function.\n",
"\n",
" >>> print([i for i in example_generator(4)])\n",
" [0, 1, 2, 3]\n",
"\n",
" \"\"\"\n",
" for i in range(n):\n",
" yield i\n",
"\n",
"type(example_generator)\n",
"print(example_generator.__doc__)\n",
"help(example_generator)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.6.1 pydoc\n",
"En la linea de comando el modulo **pydoc** permite general la documentacion de los modulo en formato html:\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"cd miModulo\n",
"pydoc -w helloWorld.py"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Transitional//EN\">\n",
"<html><head><title>Python: module helloWorld</title>\n",
"<meta charset=\"utf-8\">\n",
"</head><body bgcolor=\"#f0f0f8\">\n",
"\n",
"<table width=\"100%\" cellspacing=0 cellpadding=2 border=0 summary=\"heading\">\n",
"<tr bgcolor=\"#7799ee\">\n",
"<td valign=bottom>&nbsp;<br>\n",
"<font color=\"#ffffff\" face=\"helvetica, arial\">&nbsp;<br><big><big><strong>helloWorld</strong></big></big></font></td\n",
"><td align=right valign=bottom\n",
"><font color=\"#ffffff\" face=\"helvetica, arial\"><a href=\".\">index</a><br><a href=\"file:/home/mchc/git/tap1012/miModulo/helloWorld.py\">/home/mchc/git/tap1012/miModulo/helloWorld.py</a></font></td></tr></table>\n",
" <p><tt>This&nbsp;example&nbsp;module&nbsp;shows&nbsp;various&nbsp;types&nbsp;of&nbsp;documentation&nbsp;available&nbsp;for&nbsp;use<br>\n",
"with&nbsp;pydoc.&nbsp;&nbsp;To&nbsp;generate&nbsp;HTML&nbsp;documentation&nbsp;for&nbsp;this&nbsp;module&nbsp;issue&nbsp;the<br>\n",
"command:<br>\n",
"&nbsp;<br>\n",
"&nbsp;&nbsp;&nbsp;&nbsp;pydoc&nbsp;-w&nbsp;foo</tt></p>\n",
"<p>\n",
"<table width=\"100%\" cellspacing=0 cellpadding=2 border=0 summary=\"section\">\n",
"<tr bgcolor=\"#eeaa77\">\n",
"<td colspan=3 valign=bottom>&nbsp;<br>\n",
"<font color=\"#ffffff\" face=\"helvetica, arial\"><big><strong>Functions</strong></big></font></td></tr>\n",
" \n",
"<tr><td bgcolor=\"#eeaa77\"><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</tt></td><td>&nbsp;</td>\n",
"<td width=\"100%\"><dl><dt><a name=\"-hello\"><strong>hello</strong></a>()</dt><dd><tt>Documentacion&nbsp;de&nbsp;función&nbsp;<a href=\"#-hello\">hello</a>()&nbsp;del&nbsp;modulo&nbsp;miModulo.</tt></dd></dl>\n",
"</td></tr></table>\n",
"</body></html>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"HTML(filename=\"miModulo/helloWorld.html\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.7 Casos de Prueba (doctest)\n",
"\n",
"doctest prueba el código fuente ejecutando ejemplos incrustados en la documentación y verificando que producen los resultados esperados. Funciona al analizar el texto de ayuda para encontrar ejemplos, ejecutarlos y luego comparar el texto de salida con el valor esperado.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trying:\n",
" multiply(4, 3)\n",
"Expecting:\n",
" 12\n",
"ok\n",
"Trying:\n",
" multiply('a', 3)\n",
"Expecting:\n",
" 'aaa'\n",
"ok\n",
"1 items had no tests:\n",
" __main__\n",
"1 items passed all tests:\n",
" 2 tests in __main__.multiply\n",
"2 tests in 2 items.\n",
"2 passed and 0 failed.\n",
"Test passed.\n"
]
},
{
"data": {
"text/plain": [
"TestResults(failed=0, attempted=2)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def multiply(a, b):\n",
" '''\n",
" >>> multiply(4, 3)\n",
" 12\n",
" >>> multiply('a', 3)\n",
" 'aaa'\n",
" '''\n",
" return a * b\n",
"import doctest\n",
"\n",
"doctest.testmod(verbose=True)\n"
>>>>>>> master
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.4 Documentacion y Casos de Prueba"
"## 2.8 Ejercicios\n",
"Escribe la documentacion y los casos de prueba para todos los ejercicios de la semana 1 y 2."
]
}
],
......
.ipynb_checkpoints/03-POO-checkpoint.ipynb
View file @ f3897807
......@@ -4,8 +4,137 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Python\n",
"## 1.5 Programacion Orientada a Objetos: Clases "
"# 3. Programacion Orientada a Objetos\n",
"## 3.1 Clases \n",
"\n",
"Las clases proporcionan un medio de agrupar datos y funcionalidad. La creación de una nueva clase crea un nuevo tipo de objeto, lo que permite crear nuevas instancias de ese objeto. Cada instancia de la clase puede tener atributos adjuntos para mantener su estado. Las instancias de clase también pueden tener métodos (definidos por su clase) para modificar su estado."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"class Dog:\n",
" tricks = [] # Variable de clase\n",
" kind = 'canine' # Variable de clase\n",
" def __init__(self, name):\n",
" self.name = name # Variable de instancia\n",
" def add_trick(self, trick):\n",
" self.tricks.append(trick)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'canine'"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Dog.kind\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "type object 'Dog' has no attribute 'name'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-30-629675d46941>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: type object 'Dog' has no attribute 'name'"
]
}
],
"source": [
"Dog.name"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Max'"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Dog(\"Max\").name"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"Max = Dog(\"Max\")\n",
"Keeper = Dog(\"Keeper\")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'canine'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Max.kind"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'canine'"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Max.kind=\"felis\"\n",
"Keeper.kind"
]
},
{
......@@ -13,15 +142,44 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"Max. def add_trick(self, trick):\n",
" self.tricks.append(trick)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2 Herencia"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.6 Programacion Orientada a Objetos: Herencia y Polimorfismo"
"## 3.3 Polimorfismo"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dict_items' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-afcb87a7ff57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhelp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict_items\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'dict_items' is not defined"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
......@@ -46,7 +204,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8rc1"
"version": "3.5.3"
}
},
"nbformat": 4,
......
02-Comprehension&Documentation.ipynb
View file @ f3897807
......@@ -5,21 +5,683 @@
"metadata": {},
"source": [
"# 2. Python\n",
"## 2.1 Comprensiónes"
"## 2.1 Comprensiónes\n",
"Las comprensiones de python proveen de una forma consisa de crear listas, diccionarios y conjuntos. Su nombre proviene de teria de conjuntos en donde la *notación contructiva de conjuntos* o comprensión se define como:\n",
"\n",
"\n",
"[Wikipedia](https://en.wikipedia.org/wiki/Set-builder_notation): Definir conjuntos por propiedades también se conoce como ***comprensión de conjuntos***, abstracción de conjuntos o como definición de la intención de un conjunto.\n",
"\n",
"En python la estructura de una comprensión es la siguiente:![img](https://python-3-patterns-idioms-test.readthedocs.io/en/latest/_images/listComprehensions.gif)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2, 2, 4, 6, 8]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"old_list = [1,2,2,3,4,5,6,7,7,8,9]\n",
"new_list = []\n",
"for i in old_list:\n",
" if i%2==0:\n",
" new_list.append(i)\n",
"new_list"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2, 2, 4, 6, 8]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Lista\n",
"new_list=[i for i in old_list if i%2==0]\n",
"new_list"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: 1,\n",
" 1: 1,\n",
" 2: 4,\n",
" 3: 27,\n",
" 4: 256,\n",
" 5: 3125,\n",
" 6: 46656,\n",
" 7: 823543,\n",
" 8: 16777216,\n",
" 9: 387420489}"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Diccionario\n",
"{ i:i**i for i in range(10)}"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[1, 0, 0], [0, 1, 0], [0, 0, 1]]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Comprensión anidada\n",
"[ [ 1 if item_idx == row_idx else 0 for item_idx in range(0, 3) ] for row_idx in range(0, 3) ]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{1, 2, 3, 4, 5, 6, 7, 8, 9}"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Conjunto\n",
"new_set={i for i in old_list}\n",
"new_set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.2 Expresiones lambda $\\lambda$\n",
"Una función anónima o **expresión lambda** es una subrutina definida que no está enlazada a un identificador. Las funciones lambda generalmente son Argumentos que son pasados a otras funciónes de orden superior o Usadas para construir el resultado de una función de orden superior que necesita retornar una función [[Wikiedia](https://en.wikipedia.org/wiki/Anonymous_function)].\n",
"\n",
"En Python las expresiones lambda no pueden utilizar ciclos ni utilizar la plabra reservada **return**, su sintaxis es:```lambda <parametros>:<expresion>```\n",
"\n",
"*Nota: **filter** regresa una lista de elementos para los cuales una funcion regresa **True**; **map** aplica una funcion a todos los ementos de una lista.*\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"function"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = lambda x: x * x\n",
"type(f)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[4, 4, 256, 46656, 16777216]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(map(lambda i: i**i, filter(lambda i: i%2==0, old_list)))"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(13, -3), (4, 1), (1, 2), (9, 10)]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = [(1, 2), (4, 1), (9, 10), (13, -3)]\n",
"a.sort(key=lambda x: x[1])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[102.56, 97.7, 99.14, 100.03999999999999]\n"
]
}
],
"source": [
"Celsius = [39.2, 36.5, 37.3, 37.8]\n",
"Fahrenheit = map(lambda x: (float(9)/5)*x + 32, Celsius)\n",
"print(list(Fahrenheit))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.2 Generadores"
"## 2.3 Generadores: Funciones y Expresiones\n",
"Los Generadores son funciones o expresiones que regesan un valor iterador en lugar de un valor, las para que una funcion regrese un generador en lugar de un valor se utiliza la palabra reservada **yield** en lugar de **return**."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"499999500000\n"
]
}
],
"source": [
"def firstn(n):\n",
" num = 0\n",
" while num < n:\n",
" yield num\n",
" num += 1\n",
"\n",
"sum_of_first_n = sum(firstn(1000000))\n",
"print(sum_of_first_n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tambien es posible construir generadores utilizando expresiones utilizando parentesis en lugar de corchetes. Esto resulta util cuando utilizar una expresion generaria una lista muy grande que ocuparia mucha memoria."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n",
"0\n",
"2\n",
"4\n",
"[6, 8, 10, 12, 14, 16, 18]\n"
]
}
],
"source": [
"doublesC = [2 * n for n in range(10)]\n",
"print(doublesC)\n",
"doublesG = (2 * n for n in range(10))\n",
"print(next(doublesG))\n",
"print(next(doublesG))\n",
"print(next(doublesG))\n",
"print(list(doublesG))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.4 Ejercicios\n",
"### 2.4.1\n",
"Dada una lista de enteros **a**, utilisando una comprensión, Imprimir una nueva lista que contenga \"par\" o \"impar\" en cada uno de sus elementos, dependiendo del valor del elemento en la lista orginal."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4.2\n",
"Dada una lista *a*, utilizando una comprensión, imprimir una lista de tuplas, en donde cada tupla contiene el indice y el valor de elemento de la lista orginal."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"my_list = ['apple', 'banana', 'grapes', 'pear']\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4.3\n",
"Genera una lista que contenga una tuplas con todos los pares posibles de elementos entre las dos listas."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"a = [0,1,2,3,4,5,6]\n",
"b = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4.4 \n",
"Utilizando *filter* y una expresion *lambda* generar una lista que contenga todos los numeros impares de una lista de entrada **a**."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"a=[5, 7, 22, 97, 54, 62, 77, 23, 73, 61]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4.5\n",
"Utilizado ***reduce*** y una expresion *lambda*, obten la suma de todos lo elementos en una lista."
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"a = [5, 8, 10, 20, 50, 100] "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4.6 \n",
"Utilizando *map* y una expresion *lambda* obtener una lista cuyos elementos sean la suma de los elementos correspondientes en las listas **a** y **b**.\n"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"a = [5, 10, 15, 20]\n",
" \n",
"b = [30, 35, 40, 45]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.6.7\n",
"Escribir un generador en forma de expresion que obtenga las primeras 10 ternas pitagoricas.\n",
"\n",
"[Wikipedia](https://es.wikipedia.org/wiki/Terna_pitag%C3%B3rica): Una terna pitagórica es un conjunto ordenado de tres números enteros positivos a, b, c, y son solución de la ecuación diofantina cuadrática $a^{2}+b^{2}=c^{2}$.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.6.8 \n",
"Escribir un generador en forma de funcion recursiva que entregue la lista de todas las permitaciones de los elementos en una lista."
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"a = [\"a\", \"b\", \"c\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.5 Modulos y Paquetes\n",
"En Python, cada uno de nuestros archivos .py se denominan módulos. Estos módulos, a la vez, pueden formar parte de paquetes. Un paquete, es una carpeta que contiene archivos .py. Para que una carpeta pueda ser considerada un paquete, debe contener un archivo de inicio llamado ``__init__.py``. Este archivo, no necesita contener ninguna instrucción. De hecho, puede estar vacío.\n",
"\n",
"\n",
"``\n",
"└── miModulo\n",
" ├── __init__.py \n",
" └── helloWOrld.py \n",
"``"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n"
]
}
],
"source": [
"from miModulo import helloWorld\n",
"helloWorld.hello()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.6 Documentacion (docstring)\n",
"\n",
"Python **Docstring** es el texto de documentación que puede aprecer en la definición de una clase, módulo, función o método, y se escribe como la primera declaración. Se puede acceder a las cadenas de documenacion desde el atributo doc para cualquiera de los objetos de Python y también con la función incorporada **help()**."
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generators have a ``Yields`` section instead of a ``Returns`` section.\n",
"\n",
" Args:\n",
" n (int): The upper limit of the range to generate, from 0 to `n` - 1.\n",
"\n",
" Yields:\n",
" int: The next number in the range of 0 to `n` - 1.\n",
"\n",
" Examples:\n",
" Examples should be written in doctest format, and should illustrate how\n",
" to use the function.\n",
"\n",
" >>> print([i for i in example_generator(4)])\n",
" [0, 1, 2, 3]\n",
"\n",
" \n",
"Help on function example_generator in module __main__:\n",
"\n",
"example_generator(n)\n",
" Generators have a ``Yields`` section instead of a ``Returns`` section.\n",
" \n",
" Args:\n",
" n (int): The upper limit of the range to generate, from 0 to `n` - 1.\n",
" \n",
" Yields:\n",
" int: The next number in the range of 0 to `n` - 1.\n",
" \n",
" Examples:\n",
" Examples should be written in doctest format, and should illustrate how\n",
" to use the function.\n",
" \n",
" >>> print([i for i in example_generator(4)])\n",
" [0, 1, 2, 3]\n",
"\n"
]
}
],
"source": [
"def example_generator(n):\n",
" \"\"\"Generators have a ``Yields`` section instead of a ``Returns`` section.\n",
"\n",
" Args:\n",
" n (int): The upper limit of the range to generate, from 0 to `n` - 1.\n",
"\n",
" Yields:\n",
" int: The next number in the range of 0 to `n` - 1.\n",
"\n",
" Examples:\n",
" Examples should be written in doctest format, and should illustrate how\n",
" to use the function.\n",
"\n",
" >>> print([i for i in example_generator(4)])\n",
" [0, 1, 2, 3]\n",
"\n",
" \"\"\"\n",
" for i in range(n):\n",
" yield i\n",
"\n",
"type(example_generator)\n",
"print(example_generator.__doc__)\n",
"help(example_generator)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.6.1 pydoc\n",
"En la linea de comando el modulo **pydoc** permite general la documentacion de los modulo en formato html:\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"cd miModulo\n",
"pydoc -w helloWorld.py"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Transitional//EN\">\n",
"<html><head><title>Python: module helloWorld</title>\n",
"<meta charset=\"utf-8\">\n",
"</head><body bgcolor=\"#f0f0f8\">\n",
"\n",
"<table width=\"100%\" cellspacing=0 cellpadding=2 border=0 summary=\"heading\">\n",
"<tr bgcolor=\"#7799ee\">\n",
"<td valign=bottom>&nbsp;<br>\n",
"<font color=\"#ffffff\" face=\"helvetica, arial\">&nbsp;<br><big><big><strong>helloWorld</strong></big></big></font></td\n",
"><td align=right valign=bottom\n",
"><font color=\"#ffffff\" face=\"helvetica, arial\"><a href=\".\">index</a><br><a href=\"file:/home/mchc/git/tap1012/miModulo/helloWorld.py\">/home/mchc/git/tap1012/miModulo/helloWorld.py</a></font></td></tr></table>\n",
" <p><tt>This&nbsp;example&nbsp;module&nbsp;shows&nbsp;various&nbsp;types&nbsp;of&nbsp;documentation&nbsp;available&nbsp;for&nbsp;use<br>\n",
"with&nbsp;pydoc.&nbsp;&nbsp;To&nbsp;generate&nbsp;HTML&nbsp;documentation&nbsp;for&nbsp;this&nbsp;module&nbsp;issue&nbsp;the<br>\n",
"command:<br>\n",
"&nbsp;<br>\n",
"&nbsp;&nbsp;&nbsp;&nbsp;pydoc&nbsp;-w&nbsp;foo</tt></p>\n",
"<p>\n",
"<table width=\"100%\" cellspacing=0 cellpadding=2 border=0 summary=\"section\">\n",
"<tr bgcolor=\"#eeaa77\">\n",
"<td colspan=3 valign=bottom>&nbsp;<br>\n",
"<font color=\"#ffffff\" face=\"helvetica, arial\"><big><strong>Functions</strong></big></font></td></tr>\n",
" \n",
"<tr><td bgcolor=\"#eeaa77\"><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</tt></td><td>&nbsp;</td>\n",
"<td width=\"100%\"><dl><dt><a name=\"-hello\"><strong>hello</strong></a>()</dt><dd><tt>Documentacion&nbsp;de&nbsp;función&nbsp;<a href=\"#-hello\">hello</a>()&nbsp;del&nbsp;modulo&nbsp;miModulo.</tt></dd></dl>\n",
"</td></tr></table>\n",
"</body></html>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"HTML(filename=\"miModulo/helloWorld.html\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.7 Casos de Prueba (doctest)\n",
"\n",
"doctest prueba el código fuente ejecutando ejemplos incrustados en la documentación y verificando que producen los resultados esperados. Funciona al analizar el texto de ayuda para encontrar ejemplos, ejecutarlos y luego comparar el texto de salida con el valor esperado.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trying:\n",
" multiply(4, 3)\n",
"Expecting:\n",
" 12\n",
"ok\n",
"Trying:\n",
" multiply('a', 3)\n",
"Expecting:\n",
" 'aaa'\n",
"ok\n",
"1 items had no tests:\n",
" __main__\n",
"1 items passed all tests:\n",
" 2 tests in __main__.multiply\n",
"2 tests in 2 items.\n",
"2 passed and 0 failed.\n",
"Test passed.\n"
]
},
{
"data": {
"text/plain": [
"TestResults(failed=0, attempted=2)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def multiply(a, b):\n",
" '''\n",
" >>> multiply(4, 3)\n",
" 12\n",
" >>> multiply('a', 3)\n",
" 'aaa'\n",
" '''\n",
" return a * b\n",
"import doctest\n",
"\n",
"doctest.testmod(verbose=True)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.4 Documentacion y Casos de Prueba"
"## 2.8 Ejercicios\n",
"Escribe la documentacion y los casos de prueba para todos los ejercicios de la semana 1 y 2."
]
}
],
......
03-POO.ipynb
View file @ f3897807
......@@ -4,8 +4,137 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Python\n",
"## 1.5 Programacion Orientada a Objetos: Clases "
"# 3. Programacion Orientada a Objetos\n",
"## 3.1 Clases \n",
"\n",
"Las clases proporcionan un medio de agrupar datos y funcionalidad. La creación de una nueva clase crea un nuevo tipo de objeto, lo que permite crear nuevas instancias de ese objeto. Cada instancia de la clase puede tener atributos adjuntos para mantener su estado. Las instancias de clase también pueden tener métodos (definidos por su clase) para modificar su estado."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"class Dog:\n",
" tricks = [] # Variable de clase\n",
" kind = 'canine' # Variable de clase\n",
" def __init__(self, name):\n",
" self.name = name # Variable de instancia\n",
" def add_trick(self, trick):\n",
" self.tricks.append(trick)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'canine'"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Dog.kind\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "type object 'Dog' has no attribute 'name'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-30-629675d46941>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: type object 'Dog' has no attribute 'name'"
]
}
],
"source": [
"Dog.name"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Max'"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Dog(\"Max\").name"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"Max = Dog(\"Max\")\n",
"Keeper = Dog(\"Keeper\")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'canine'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Max.kind"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'canine'"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Max.kind=\"felis\"\n",
"Keeper.kind"
]
},
{
......@@ -13,15 +142,44 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
"source": [
"Max. def add_trick(self, trick):\n",
" self.tricks.append(trick)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2 Herencia"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.6 Programacion Orientada a Objetos: Herencia y Polimorfismo"
"## 3.3 Polimorfismo"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dict_items' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-afcb87a7ff57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhelp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict_items\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'dict_items' is not defined"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
......@@ -46,7 +204,11 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
<<<<<<< HEAD
"version": "3.5.2"
=======
"version": "3.5.3"
>>>>>>> master
}
},
"nbformat": 4,
......
miModulo/__init__py 0 → 100644
View file @ f3897807
"""
This example module shows various types of documentation available for use
with pydoc. To generate HTML documentation for this module issue the
command:
pydoc -w foo
"""
miModulo/helloWorld.html 0 → 100644
View file @ f3897807
<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
<html><head><title>Python: module helloWorld</title>
<meta charset="utf-8">
</head><body bgcolor="#f0f0f8">
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="heading">
<tr bgcolor="#7799ee">
<td valign=bottom>&nbsp;<br>
<font color="#ffffff" face="helvetica, arial">&nbsp;<br><big><big><strong>helloWorld</strong></big></big></font></td
><td align=right valign=bottom
><font color="#ffffff" face="helvetica, arial"><a href=".">index</a><br><a href="file:/home/mchc/git/tap1012/miModulo/helloWorld.py">/home/mchc/git/tap1012/miModulo/helloWorld.py</a></font></td></tr></table>
<p><tt>This&nbsp;example&nbsp;module&nbsp;shows&nbsp;various&nbsp;types&nbsp;of&nbsp;documentation&nbsp;available&nbsp;for&nbsp;use<br>
with&nbsp;pydoc.&nbsp;&nbsp;To&nbsp;generate&nbsp;HTML&nbsp;documentation&nbsp;for&nbsp;this&nbsp;module&nbsp;issue&nbsp;the<br>
command:<br>
&nbsp;<br>
&nbsp;&nbsp;&nbsp;&nbsp;pydoc&nbsp;-w&nbsp;foo</tt></p>
<p>
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
<tr bgcolor="#eeaa77">
<td colspan=3 valign=bottom>&nbsp;<br>
<font color="#ffffff" face="helvetica, arial"><big><strong>Functions</strong></big></font></td></tr>
<tr><td bgcolor="#eeaa77"><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</tt></td><td>&nbsp;</td>
<td width="100%"><dl><dt><a name="-hello"><strong>hello</strong></a>()</dt><dd><tt>Documentacion&nbsp;de&nbsp;función&nbsp;<a href="#-hello">hello</a>()&nbsp;del&nbsp;modulo&nbsp;miModulo.</tt></dd></dl>
</td></tr></table>
</body></html>
\ No newline at end of file
miModulo/helloWorld.py 0 → 100644
View file @ f3897807
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
This example module shows various types of documentation available for use
with pydoc. To generate HTML documentation for this module issue the
command:
pydoc -w foo
"""
def hello():
'''
Documentacion de función hello() del modulo miModulo.
'''
print("hello")
tarjetas/src/Git_icon.svg 0 → 100644
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tarjetas/src/git.svg 0 → 100644
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tarjetas/src/penguin.svg 0 → 100644
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tarjetas/src/scrapy-logo.svg 0 → 100644
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