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02-Comprehension&Documentation.ipynb
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"metadata": {}, "metadata": {},
"source": [ "source": [
"# 2. Python\n", "# 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": [
"## 2.3 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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## 2.2 Generadores" "### 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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "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."
] ]
}, },
{ {
......
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