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@Scot3004
Last active August 28, 2023 23:31
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poo_en_python_with_plant.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/Scot3004/5d57573d97ed859afc5dd662556bf14c/poo_en_python_with_plant.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FaROxeyg3HRi"
},
"source": [
"# Python orientado a objetos\n",
"\n",
"Hola, El dia de hoy vamos a estar aprendiendo a programar en **python** usando **jupyter notebook**, e instalaremos python en nuestras maquinas, de momento no nos preocuparemos por entornos virtuales y nos apoyaremos en la instalación que tenga nuestro sistema operativo\n",
"\n",
"El objetivo del dia de hoy es aprender sobre la creación de objetos en python"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3RqEWyvy3HRm"
},
"source": [
"# Instalación de python en windows\n",
"\n",
"Colocar captura de como instale python en mi pc"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kYnYXuge3HRm"
},
"source": [
"# Python en VS code\n",
"\n",
"Visual studio code es un editor creado por Microsoft el cual permite el uso de extensiones para facilitar el desarrollo.\n",
"Entre las extensiones que podemos instalar en VS Code están las extensiones de python y jupyter\n",
"\n",
"Python: https://marketplace.visualstudio.com/items?itemName=ms-python.python\n",
"Jupyter: https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter\n",
"\n",
"Recomiendo la instalación de ambas incluyendo el paquete de extensiones de jupyter que trae los renderers para la salida de los notebooks\n",
"https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter-renderers\n",
"\n",
"![image.png](data:image/png;base64,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)\n",
"\n",
"Si descargamos python desde microsoft store debemos adicionar la ruta donde se instala python al path, en mi caso es `C:\\Users\\sergio.orozcot\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\Scripts`\n",
"\n",
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]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qZMwMMAy-hkn"
},
"source": [
"Para esta sesión nos apoyaremos en plant uml entonces por favor ejecutar la siguiente linea"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gXoJAuvO-gvS",
"outputId": "5b290a57-9085-4f5a-e8ae-4f15754b1c66"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting iplantuml\n",
" Downloading IPlantUML-0.1.1.tar.gz (5.5 kB)\n",
"Collecting plantweb\n",
" Downloading plantweb-1.2.1-py3-none-any.whl (20 kB)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plantweb->iplantuml) (1.15.0)\n",
"Requirement already satisfied: docutils in /usr/local/lib/python3.7/dist-packages (from plantweb->iplantuml) (0.17.1)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from plantweb->iplantuml) (2.23.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->plantweb->iplantuml) (2022.6.15)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->plantweb->iplantuml) (2.10)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->plantweb->iplantuml) (1.24.3)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->plantweb->iplantuml) (3.0.4)\n",
"Building wheels for collected packages: iplantuml\n",
" Building wheel for iplantuml (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for iplantuml: filename=IPlantUML-0.1.1-py2.py3-none-any.whl size=4910 sha256=976a41688f4de34923b643fe30cd57437ba613724d7e52558dc2ea135b53eef0\n",
" Stored in directory: /root/.cache/pip/wheels/cf/64/08/5bac65794ab011a60f7ef62413d3c430cf715345028f4b3914\n",
"Successfully built iplantuml\n",
"Installing collected packages: plantweb, iplantuml\n",
"Successfully installed iplantuml-0.1.1 plantweb-1.2.1\n"
]
}
],
"source": [
"!pip install iplantuml"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "3EjbYLifDgnm"
},
"outputs": [],
"source": [
"import iplantuml"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UzdmSi7A3HRn"
},
"source": [
"# Que la programación orientada a objetos\n",
"\n",
"Es un paradigma de programación que parte del concepto de \"objetos\" como base, los cuales contienen información en forma de campos (a veces también referidos como atributos o propiedades) y código en forma de métodos."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "fuD1g5V8-rKq",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 107
},
"outputId": "ca54441a-03c1-4f76-cb3a-1bc535d8691b"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<IPython.core.display.SVG object>"
],
"image/svg+xml": "<svg contentStyleType=\"text/css\" height=\"85px\" preserveAspectRatio=\"none\" style=\"width:92px;height:85px;background:#FFFFFF;\" version=\"1.1\" viewBox=\"0 0 92 85\" width=\"92px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" zoomAndPan=\"magnify\"><defs/><g><!--MD5=[838fb693084ebcda77f8479951cb9fdf]\nclass Perro--><g id=\"elem_Perro\"><rect fill=\"#F1F1F1\" height=\"64.2969\" id=\"Perro\" rx=\"2.5\" ry=\"2.5\" style=\"stroke:#181818;stroke-width:0.5;\" width=\"71\" x=\"7\" y=\"7\"/><ellipse cx=\"22\" cy=\"23\" fill=\"#ADD1B2\" rx=\"11\" ry=\"11\" style=\"stroke:#181818;stroke-width:1.0;\"/><path d=\"M24.3438,18.6719 C23.4063,18.2344 22.8125,18.0938 21.9375,18.0938 C19.3125,18.0938 17.3125,20.1719 17.3125,22.8906 L17.3125,24.0156 C17.3125,26.5938 19.4219,28.4844 22.3125,28.4844 C23.5313,28.4844 24.6875,28.1875 25.4375,27.6406 C26.0156,27.2344 26.3438,26.7813 26.3438,26.3906 C26.3438,25.9375 25.9531,25.5469 25.4844,25.5469 C25.2656,25.5469 25.0625,25.625 24.875,25.8125 C24.4219,26.2969 24.4219,26.2969 24.2344,26.3906 C23.8125,26.6563 23.125,26.7813 22.3594,26.7813 C20.3125,26.7813 19.0156,25.6875 19.0156,23.9844 L19.0156,22.8906 C19.0156,21.1094 20.2656,19.7969 22,19.7969 C22.5781,19.7969 23.1875,19.9531 23.6563,20.2031 C24.1406,20.4844 24.3125,20.7031 24.4063,21.1094 C24.4688,21.5156 24.5,21.6406 24.6406,21.7656 C24.7813,21.9063 25.0156,22.0156 25.2344,22.0156 C25.5,22.0156 25.7656,21.875 25.9375,21.6563 C26.0469,21.5 26.0781,21.3125 26.0781,20.8906 L26.0781,19.4688 C26.0781,19.0313 26.0625,18.9063 25.9688,18.75 C25.8125,18.4844 25.5313,18.3438 25.2344,18.3438 C24.9375,18.3438 24.7344,18.4375 24.5156,18.75 L24.3438,18.6719 Z \" fill=\"#000000\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"39\" x=\"36\" y=\"27.8467\">Perro</text><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"8\" x2=\"77\" y1=\"39\" y2=\"39\"/><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"8\" x2=\"77\" y1=\"47\" y2=\"47\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"51\" x=\"13\" y=\"63.9951\">ladrar()</text></g><!--MD5=[840edd25c241ac881d81ba89d8e70a4e]\n@startuml\nPerro : ladrar()\n@enduml\n\nPlantUML version 1.2022.7beta2(Unknown compile time)\n(GPL source distribution)\nJava Runtime: Java(TM) SE Runtime Environment\nJVM: Java HotSpot(TM) 64-Bit Server VM\nDefault Encoding: UTF-8\nLanguage: en\nCountry: US\n--></g></svg>"
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"%%plantuml\n",
"\n",
"@startuml\n",
"Perro : ladrar()\n",
"@enduml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5Ii3_JeW3HRn",
"outputId": "e8703747-877b-46fa-de2d-8847f7f21c02"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"guau guau!\n"
]
}
],
"source": [
"class Perro:\n",
" def ladrar(self):\n",
" print(\"guau guau!\")\n",
"\n",
"perro = Perro()\n",
"perro.ladrar()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SKYyByo-3HRp"
},
"source": [
"Python es un lenguaje donde toca ser explicitos, y en cada metodo de clase es necesario agregar la referencia al objeto que por convención en python llamamos `self`, lo que en algunos lenguajes de progamación llamariamos `this`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2pV1bwnX3HRp",
"outputId": "ae9c2f01-5618-4f5a-bb3e-f5aa9bd652bb"
},
"outputs": [
{
"ename": "TypeError",
"evalue": "Perro1.ladrar() takes 0 positional arguments but 1 was given",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32md:\\intro python\\poo_en_python_with_plant.ipynb Cell 10'\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/intro%20python/poo_en_python_with_plant.ipynb#ch0000009?line=2'>3</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mguau guau!\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/intro%20python/poo_en_python_with_plant.ipynb#ch0000009?line=4'>5</a>\u001b[0m perro \u001b[39m=\u001b[39m Perro1()\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/intro%20python/poo_en_python_with_plant.ipynb#ch0000009?line=5'>6</a>\u001b[0m perro\u001b[39m.\u001b[39;49mladrar()\n",
"\u001b[1;31mTypeError\u001b[0m: Perro1.ladrar() takes 0 positional arguments but 1 was given"
]
}
],
"source": [
"class Perro1:\n",
" def ladrar():\n",
" print(\"guau guau!\")\n",
"\n",
"perro = Perro1()\n",
"perro.ladrar()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KMIyZVg23HRp"
},
"source": [
"# Metodos estaticos\n",
"\n",
"Python soporta metodos estaticos. En python como tal para los utilitarios no necesitamos encapsularlo dentro de clases, ya que de por si las funciones son objetos pero si queremos lo podemos hacer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dGo-4PtA3HRq",
"outputId": "e232cf16-c1c1-413a-9af6-a2d66bfb37cc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"algo\n"
]
}
],
"source": [
"class Util:\n",
" @staticmethod\n",
" def hacer_algo():\n",
" print(\"algo\")\n",
"\n",
"Util.hacer_algo()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5XknMAP73HRq"
},
"source": [
"# Metodos magicos\n",
"\n",
"Son metodos especiales definidos por python y estan prefijados con `__`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9G38NXK83HRr",
"outputId": "83a61772-305d-4832-a7d8-76681033bd03"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n",
"25\n"
]
}
],
"source": [
"class Specialstring:\n",
" def __len__(self):\n",
" return 25\n",
"\n",
"normal_string = \"Sergio\"\n",
"print(len(normal_string))\n",
"\n",
"string = Specialstring()\n",
"print(len(string))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mIFxKdpd3HRr"
},
"source": [
"# Atributos de clases\n",
"\n",
"Los atributos de clase en python normalmente se definen en el constructor `__init__` y en dicho metodo se coloca el tipo de datos en caso de usar type hints"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ii8OIdgHDgnr",
"outputId": "09519e4c-8f4f-4ec2-e37a-376b3be81966"
},
"outputs": [
{
"data": {
"image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" contentStyleType=\"text/css\" height=\"134px\" preserveAspectRatio=\"none\" style=\"width:128px;height:134px;background:#FFFFFF;\" version=\"1.1\" viewBox=\"0 0 128 134\" width=\"128px\" zoomAndPan=\"magnify\"><defs/><g><!--MD5=[8f63dadf5c643e74a34e53c8f0a15d23]\nclass Persona--><g id=\"elem_Persona\"><rect fill=\"#F1F1F1\" height=\"113.1875\" id=\"Persona\" rx=\"2.5\" ry=\"2.5\" style=\"stroke:#181818;stroke-width:0.5;\" width=\"107\" x=\"7\" y=\"7\"/><ellipse cx=\"30.1\" cy=\"23\" fill=\"#ADD1B2\" rx=\"11\" ry=\"11\" style=\"stroke:#181818;stroke-width:1.0;\"/><path d=\"M32.4438,18.6719 C31.5063,18.2344 30.9125,18.0938 30.0375,18.0938 C27.4125,18.0938 25.4125,20.1719 25.4125,22.8906 L25.4125,24.0156 C25.4125,26.5938 27.5219,28.4844 30.4125,28.4844 C31.6313,28.4844 32.7875,28.1875 33.5375,27.6406 C34.1156,27.2344 34.4438,26.7813 34.4438,26.3906 C34.4438,25.9375 34.0531,25.5469 33.5844,25.5469 C33.3656,25.5469 33.1625,25.625 32.975,25.8125 C32.5219,26.2969 32.5219,26.2969 32.3344,26.3906 C31.9125,26.6563 31.225,26.7813 30.4594,26.7813 C28.4125,26.7813 27.1156,25.6875 27.1156,23.9844 L27.1156,22.8906 C27.1156,21.1094 28.3656,19.7969 30.1,19.7969 C30.6781,19.7969 31.2875,19.9531 31.7563,20.2031 C32.2406,20.4844 32.4125,20.7031 32.5063,21.1094 C32.5688,21.5156 32.6,21.6406 32.7406,21.7656 C32.8813,21.9063 33.1156,22.0156 33.3344,22.0156 C33.6,22.0156 33.8656,21.875 34.0375,21.6563 C34.1469,21.5 34.1781,21.3125 34.1781,20.8906 L34.1781,19.4688 C34.1781,19.0313 34.1625,18.9063 34.0688,18.75 C33.9125,18.4844 33.6313,18.3438 33.3344,18.3438 C33.0375,18.3438 32.8344,18.4375 32.6156,18.75 L32.4438,18.6719 Z \" fill=\"#000000\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"57\" x=\"45.9\" y=\"27.8467\">Persona</text><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"8\" x2=\"113\" y1=\"39\" y2=\"39\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"57\" x=\"13\" y=\"55.9951\">nombre</text><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"57\" x=\"13\" y=\"72.292\">apellido</text><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"8\" x2=\"113\" y1=\"79.5938\" y2=\"79.5938\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"84\" x=\"13\" y=\"96.5889\">__str__(self)</text><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"95\" x=\"13\" y=\"112.8857\">__repr__(self)</text></g><!--MD5=[e2261d8b661a98c419189cc2f7f4b676]\n@startuml\nPersona : nombre\nPersona : apellido\n\nPersona : __str__(self)\nPersona : __repr__(self)\n@enduml\n\nPlantUML version 1.2022.7beta2(Unknown compile time)\n(GPL source distribution)\nJava Runtime: Java(TM) SE Runtime Environment\nJVM: Java HotSpot(TM) 64-Bit Server VM\nDefault Encoding: UTF-8\nLanguage: en\nCountry: US\n--></g></svg>",
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%plantuml\n",
"\n",
"@startuml\n",
"Persona : nombre\n",
"Persona : apellido\n",
"\n",
"Persona : __str__(self)\n",
"Persona : __repr__(self)\n",
"@enduml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "AHX0ZMuq3HRr",
"outputId": "5b38becf-7dc0-445d-9c18-e7120bee4525"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sergio Orozco\n"
]
}
],
"source": [
"class Persona:\n",
" def __init__(self, nombre, apellido):\n",
" self.nombre = nombre\n",
" self.apellido = apellido\n",
" \n",
" def __str__(self):\n",
" return \"{} {}\".format(self.nombre ,self.apellido)\n",
"\n",
" def __repr__(self):\n",
" return \"Persona('{}', '{}')\".format(self.nombre, self.apellido)\n",
"\n",
"sergio = Persona(\"Sergio\", \"Orozco\")\n",
"\n",
"print(sergio)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f8FqV3g93HRs"
},
"source": [
"# Sobrecarga de operadores\n",
"\n",
"La sobrecarga, en el contexto de la programación, se refiere a la capacidad de una función o de un operador de comportarse de diferentes maneras dependiendo de los parámetros que se pasan a la función o de los operandos sobre los que actúa el operador.\n",
"Por ejemplo podemos definir la suma de 2 Puntos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5t0tkmR7Dgns",
"outputId": "eb630e1c-7d28-46d2-8ea0-ad8f1b28797c"
},
"outputs": [
{
"data": {
"image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" contentStyleType=\"text/css\" height=\"150px\" preserveAspectRatio=\"none\" style=\"width:128px;height:150px;background:#FFFFFF;\" version=\"1.1\" viewBox=\"0 0 128 150\" width=\"128px\" zoomAndPan=\"magnify\"><defs/><g><!--MD5=[d4f042c1080df559351a114b3e747b8d]\nclass Punto--><g id=\"elem_Punto\"><rect fill=\"#F1F1F1\" height=\"129.4844\" id=\"Punto\" rx=\"2.5\" ry=\"2.5\" style=\"stroke:#181818;stroke-width:0.5;\" width=\"107\" x=\"7\" y=\"7\"/><ellipse cx=\"36.85\" cy=\"23\" fill=\"#ADD1B2\" rx=\"11\" ry=\"11\" style=\"stroke:#181818;stroke-width:1.0;\"/><path d=\"M39.1938,18.6719 C38.2563,18.2344 37.6625,18.0938 36.7875,18.0938 C34.1625,18.0938 32.1625,20.1719 32.1625,22.8906 L32.1625,24.0156 C32.1625,26.5938 34.2719,28.4844 37.1625,28.4844 C38.3813,28.4844 39.5375,28.1875 40.2875,27.6406 C40.8656,27.2344 41.1938,26.7813 41.1938,26.3906 C41.1938,25.9375 40.8031,25.5469 40.3344,25.5469 C40.1156,25.5469 39.9125,25.625 39.725,25.8125 C39.2719,26.2969 39.2719,26.2969 39.0844,26.3906 C38.6625,26.6563 37.975,26.7813 37.2094,26.7813 C35.1625,26.7813 33.8656,25.6875 33.8656,23.9844 L33.8656,22.8906 C33.8656,21.1094 35.1156,19.7969 36.85,19.7969 C37.4281,19.7969 38.0375,19.9531 38.5063,20.2031 C38.9906,20.4844 39.1625,20.7031 39.2563,21.1094 C39.3188,21.5156 39.35,21.6406 39.4906,21.7656 C39.6313,21.9063 39.8656,22.0156 40.0844,22.0156 C40.35,22.0156 40.6156,21.875 40.7875,21.6563 C40.8969,21.5 40.9281,21.3125 40.9281,20.8906 L40.9281,19.4688 C40.9281,19.0313 40.9125,18.9063 40.8188,18.75 C40.6625,18.4844 40.3813,18.3438 40.0844,18.3438 C39.7875,18.3438 39.5844,18.4375 39.3656,18.75 L39.1938,18.6719 Z \" fill=\"#000000\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"42\" x=\"54.15\" y=\"27.8467\">Punto</text><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"8\" x2=\"113\" y1=\"39\" y2=\"39\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"8\" x=\"13\" y=\"55.9951\">x</text><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"8\" x=\"13\" y=\"72.292\">y</text><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"8\" x2=\"113\" y1=\"79.5938\" y2=\"79.5938\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"90\" x=\"13\" y=\"96.5889\">__add__(self)</text><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"84\" x=\"13\" y=\"112.8857\">__str__(self)</text><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"95\" x=\"13\" y=\"129.1826\">__repr__(self)</text></g><!--MD5=[a2a9e9ce8b16f0d8f67d0fb75ca726b9]\n@startuml\nPunto : x\nPunto : y\nPunto : __add__(self)\nPunto : __str__(self)\nPunto : __repr__(self)\n@enduml\n\nPlantUML version 1.2022.7beta2(Unknown compile time)\n(GPL source distribution)\nJava Runtime: Java(TM) SE Runtime Environment\nJVM: Java HotSpot(TM) 64-Bit Server VM\nDefault Encoding: UTF-8\nLanguage: en\nCountry: US\n--></g></svg>",
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%plantuml\n",
"\n",
"@startuml\n",
"Punto : x\n",
"Punto : y\n",
"Punto : __add__(self)\n",
"Punto : __str__(self)\n",
"Punto : __repr__(self)\n",
"@enduml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bmAMLbMQ3HRs",
"outputId": "5a1431a6-2595-4bd7-b77d-c3281254947c"
},
"outputs": [
{
"ename": "TypeError",
"evalue": "unsupported operand type(s) for +: 'Punto' and 'Punto'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32md:\\intro python\\poo-en-python.ipynb Cell 16'\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/intro%20python/poo-en-python.ipynb#ch0000015?line=5'>6</a>\u001b[0m p1 \u001b[39m=\u001b[39m Punto(\u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/intro%20python/poo-en-python.ipynb#ch0000015?line=6'>7</a>\u001b[0m p2 \u001b[39m=\u001b[39m Punto(\u001b[39m3\u001b[39m, \u001b[39m4\u001b[39m)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/intro%20python/poo-en-python.ipynb#ch0000015?line=8'>9</a>\u001b[0m \u001b[39mprint\u001b[39m(p1\u001b[39m+\u001b[39;49mp2)\n",
"\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'Punto' and 'Punto'"
]
}
],
"source": [
"class Punto:\n",
" def __init__(self, x, y):\n",
" self.x = x\n",
" self.y = y\n",
"\n",
"p1 = Punto(1, 2)\n",
"p2 = Punto(3, 4)\n",
"\n",
"print(p1+p2) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "r_EqbZjP3HRs",
"outputId": "e73d84ec-9ac7-44b0-829a-8e4a83376d30"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Punto(8,6)\n",
"Punto(2,-2)\n"
]
}
],
"source": [
"class Punto:\n",
" def __init__(self, x, y):\n",
" self.x = x\n",
" self.y = y\n",
" def __add__(self, other):\n",
" return Punto(self.x + other.x, self.y + other.y)\n",
" def __sub__(self, other):\n",
" return Punto(self.x - other.x, self.y - other.y)\n",
" def __repr__(self):\n",
" return \"Punto({},{})\".format(self.x, self.y)\n",
"\n",
"p1 = Punto(5, 2)\n",
"p2 = Punto(3, 4)\n",
"\n",
"print(p1 + p2)\n",
"print(p1 - p2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "00NtQu-O3HRs"
},
"source": [
"# Sobrecarga del operador + en colecciones\n",
"\n",
"En las colecciónes el operador + se usa para crear una nueva lista uniendo los elementos de ambas listas"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nbWoRPO73HRs",
"outputId": "d6a3bcdf-a8fc-4bdf-d4df-f57300d05f7d"
},
"outputs": [
{
"data": {
"text/plain": [
"(3, 4, 2, 6)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(3,4)+(2,6)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PGnqiACk3HRt",
"outputId": "3943d40e-413e-446e-a7e7-19599c392c12"
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[1, 2] + [3, 4]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LL_TAQz03HRt",
"outputId": "9670a5a3-67ba-4b1d-fcc8-8f5c01ba8ed0"
},
"outputs": [
{
"ename": "TypeError",
"evalue": "unsupported operand type(s) for +: 'dict' and 'dict'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32md:\\intro python\\poo-en-python.ipynb Cell 5'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/intro%20python/poo-en-python.ipynb#ch0000004?line=0'>1</a>\u001b[0m {\u001b[39m\"\u001b[39;49m\u001b[39mnombre\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39m\"\u001b[39;49m\u001b[39mSergio\u001b[39;49m\u001b[39m\"\u001b[39;49m} \u001b[39m+\u001b[39;49m {\u001b[39m\"\u001b[39;49m\u001b[39mapellido\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39m\"\u001b[39;49m\u001b[39mOrozco\u001b[39;49m\u001b[39m\"\u001b[39;49m}\n",
"\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'dict' and 'dict'"
]
}
],
"source": [
"{\"nombre\": \"Sergio\"} + {\"apellido\": \"Orozco\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "H3yImEj93HRt"
},
"source": [
"# Herencia y polimorfismo\n",
"\n",
"La herencia es un mecanismo de relación entre clases donde decimos que una clase es un subtipo de otra, por ejemplo como decir que \"Un gato es un Animal\"\n",
"\n",
"La herencia nos permite para heredar (tomar del padre): campos de datos, métodos de acceso de otra clase, ademas de poder añadir nuestros propios métodos y campos. Por lo tanto la herencia nos proporciona una manera de organizar el código para no tener que volver a repetir código."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "ZgZfE9Pf_MiS",
"outputId": "d00fc6ce-c130-45d2-a664-8ddff5012bfd"
},
"outputs": [
{
"data": {
"image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" contentStyleType=\"text/css\" height=\"186px\" preserveAspectRatio=\"none\" style=\"width:187px;height:186px;background:#FFFFFF;\" version=\"1.1\" viewBox=\"0 0 187 186\" width=\"187px\" zoomAndPan=\"magnify\"><defs/><g><!--MD5=[f1ee7a876834964f16097b5e53f21668]\nclass AnimalTerrestre--><g id=\"elem_AnimalTerrestre\"><rect fill=\"#F1F1F1\" height=\"64.2969\" id=\"AnimalTerrestre\" rx=\"2.5\" ry=\"2.5\" style=\"stroke:#181818;stroke-width:0.5;\" width=\"149\" x=\"18\" y=\"7\"/><ellipse cx=\"33\" cy=\"23\" fill=\"#ADD1B2\" rx=\"11\" ry=\"11\" style=\"stroke:#181818;stroke-width:1.0;\"/><path d=\"M35.3438,18.6719 C34.4063,18.2344 33.8125,18.0938 32.9375,18.0938 C30.3125,18.0938 28.3125,20.1719 28.3125,22.8906 L28.3125,24.0156 C28.3125,26.5938 30.4219,28.4844 33.3125,28.4844 C34.5313,28.4844 35.6875,28.1875 36.4375,27.6406 C37.0156,27.2344 37.3438,26.7813 37.3438,26.3906 C37.3438,25.9375 36.9531,25.5469 36.4844,25.5469 C36.2656,25.5469 36.0625,25.625 35.875,25.8125 C35.4219,26.2969 35.4219,26.2969 35.2344,26.3906 C34.8125,26.6563 34.125,26.7813 33.3594,26.7813 C31.3125,26.7813 30.0156,25.6875 30.0156,23.9844 L30.0156,22.8906 C30.0156,21.1094 31.2656,19.7969 33,19.7969 C33.5781,19.7969 34.1875,19.9531 34.6563,20.2031 C35.1406,20.4844 35.3125,20.7031 35.4063,21.1094 C35.4688,21.5156 35.5,21.6406 35.6406,21.7656 C35.7813,21.9063 36.0156,22.0156 36.2344,22.0156 C36.5,22.0156 36.7656,21.875 36.9375,21.6563 C37.0469,21.5 37.0781,21.3125 37.0781,20.8906 L37.0781,19.4688 C37.0781,19.0313 37.0625,18.9063 36.9688,18.75 C36.8125,18.4844 36.5313,18.3438 36.2344,18.3438 C35.9375,18.3438 35.7344,18.4375 35.5156,18.75 L35.3438,18.6719 Z \" fill=\"#000000\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"117\" x=\"47\" y=\"27.8467\">AnimalTerrestre</text><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"19\" x2=\"166\" y1=\"39\" y2=\"39\"/><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"19\" x2=\"166\" y1=\"47\" y2=\"47\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"67\" x=\"24\" y=\"63.9951\">caminar()</text></g><!--MD5=[911fe1357c24e8165700ac79875f9a94]\nclass Gato--><g id=\"elem_Gato\"><rect fill=\"#F1F1F1\" height=\"48\" id=\"Gato\" rx=\"2.5\" ry=\"2.5\" style=\"stroke:#181818;stroke-width:0.5;\" width=\"67\" x=\"7\" y=\"131\"/><ellipse cx=\"22\" cy=\"147\" fill=\"#ADD1B2\" rx=\"11\" ry=\"11\" style=\"stroke:#181818;stroke-width:1.0;\"/><path d=\"M24.3438,142.6719 C23.4063,142.2344 22.8125,142.0938 21.9375,142.0938 C19.3125,142.0938 17.3125,144.1719 17.3125,146.8906 L17.3125,148.0156 C17.3125,150.5938 19.4219,152.4844 22.3125,152.4844 C23.5313,152.4844 24.6875,152.1875 25.4375,151.6406 C26.0156,151.2344 26.3438,150.7813 26.3438,150.3906 C26.3438,149.9375 25.9531,149.5469 25.4844,149.5469 C25.2656,149.5469 25.0625,149.625 24.875,149.8125 C24.4219,150.2969 24.4219,150.2969 24.2344,150.3906 C23.8125,150.6563 23.125,150.7813 22.3594,150.7813 C20.3125,150.7813 19.0156,149.6875 19.0156,147.9844 L19.0156,146.8906 C19.0156,145.1094 20.2656,143.7969 22,143.7969 C22.5781,143.7969 23.1875,143.9531 23.6563,144.2031 C24.1406,144.4844 24.3125,144.7031 24.4063,145.1094 C24.4688,145.5156 24.5,145.6406 24.6406,145.7656 C24.7813,145.9063 25.0156,146.0156 25.2344,146.0156 C25.5,146.0156 25.7656,145.875 25.9375,145.6563 C26.0469,145.5 26.0781,145.3125 26.0781,144.8906 L26.0781,143.4688 C26.0781,143.0313 26.0625,142.9063 25.9688,142.75 C25.8125,142.4844 25.5313,142.3438 25.2344,142.3438 C24.9375,142.3438 24.7344,142.4375 24.5156,142.75 L24.3438,142.6719 Z \" fill=\"#000000\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"35\" x=\"36\" y=\"151.8467\">Gato</text><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"8\" x2=\"73\" y1=\"163\" y2=\"163\"/><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"8\" x2=\"73\" y1=\"171\" y2=\"171\"/></g><!--MD5=[838fb693084ebcda77f8479951cb9fdf]\nclass Perro--><g id=\"elem_Perro\"><rect fill=\"#F1F1F1\" height=\"48\" id=\"Perro\" rx=\"2.5\" ry=\"2.5\" style=\"stroke:#181818;stroke-width:0.5;\" width=\"71\" x=\"109\" y=\"131\"/><ellipse cx=\"124\" cy=\"147\" fill=\"#ADD1B2\" rx=\"11\" ry=\"11\" style=\"stroke:#181818;stroke-width:1.0;\"/><path d=\"M126.3438,142.6719 C125.4063,142.2344 124.8125,142.0938 123.9375,142.0938 C121.3125,142.0938 119.3125,144.1719 119.3125,146.8906 L119.3125,148.0156 C119.3125,150.5938 121.4219,152.4844 124.3125,152.4844 C125.5313,152.4844 126.6875,152.1875 127.4375,151.6406 C128.0156,151.2344 128.3438,150.7813 128.3438,150.3906 C128.3438,149.9375 127.9531,149.5469 127.4844,149.5469 C127.2656,149.5469 127.0625,149.625 126.875,149.8125 C126.4219,150.2969 126.4219,150.2969 126.2344,150.3906 C125.8125,150.6563 125.125,150.7813 124.3594,150.7813 C122.3125,150.7813 121.0156,149.6875 121.0156,147.9844 L121.0156,146.8906 C121.0156,145.1094 122.2656,143.7969 124,143.7969 C124.5781,143.7969 125.1875,143.9531 125.6563,144.2031 C126.1406,144.4844 126.3125,144.7031 126.4063,145.1094 C126.4688,145.5156 126.5,145.6406 126.6406,145.7656 C126.7813,145.9063 127.0156,146.0156 127.2344,146.0156 C127.5,146.0156 127.7656,145.875 127.9375,145.6563 C128.0469,145.5 128.0781,145.3125 128.0781,144.8906 L128.0781,143.4688 C128.0781,143.0313 128.0625,142.9063 127.9688,142.75 C127.8125,142.4844 127.5313,142.3438 127.2344,142.3438 C126.9375,142.3438 126.7344,142.4375 126.5156,142.75 L126.3438,142.6719 Z \" fill=\"#000000\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"39\" x=\"138\" y=\"151.8467\">Perro</text><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"110\" x2=\"179\" y1=\"163\" y2=\"163\"/><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"110\" x2=\"179\" y1=\"171\" y2=\"171\"/></g><!--MD5=[82fe76b909222c2daf50c62607410c17]\nreverse link AnimalTerrestre to Gato--><g id=\"link_AnimalTerrestre_Gato\"><path codeLine=\"1\" d=\"M69.9664,89.4007 C63.392,103.8138 56.5165,118.8869 51.0621,130.8446 \" fill=\"none\" id=\"AnimalTerrestre-backto-Gato\" style=\"stroke:#181818;stroke-width:1.0;\"/><polygon fill=\"none\" points=\"63.6219,86.4423,78.2908,71.151,76.3593,92.2524,63.6219,86.4423\" style=\"stroke:#181818;stroke-width:1.0;\"/></g><!--MD5=[8730a4bc0bb4699bb9f0fc41efc6c2e0]\nreverse link AnimalTerrestre to Perro--><g id=\"link_AnimalTerrestre_Perro\"><path codeLine=\"2\" d=\"M115.034,89.4007 C121.608,103.8138 128.483,118.8869 133.938,130.8446 \" fill=\"none\" id=\"AnimalTerrestre-backto-Perro\" style=\"stroke:#181818;stroke-width:1.0;\"/><polygon fill=\"none\" points=\"108.641,92.2524,106.7092,71.151,121.378,86.4423,108.641,92.2524\" style=\"stroke:#181818;stroke-width:1.0;\"/></g><!--MD5=[288992cf8ee0c6cad174cecb07101f9c]\n@startuml\nAnimalTerrestre <|- - Gato\nAnimalTerrestre <|- - Perro\n\nAnimalTerrestre : caminar()\n@enduml\n\nPlantUML version 1.2022.7beta2(Unknown compile time)\n(GPL source distribution)\nJava Runtime: Java(TM) SE Runtime Environment\nJVM: Java HotSpot(TM) 64-Bit Server VM\nDefault Encoding: UTF-8\nLanguage: en\nCountry: US\n--></g></svg>",
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%plantuml\n",
"\n",
"@startuml\n",
"AnimalTerrestre <|-- Gato\n",
"AnimalTerrestre <|-- Perro\n",
"\n",
"AnimalTerrestre : caminar()\n",
"@enduml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PjufjrgY3HRt",
"outputId": "f8b397aa-cf14-417d-b86b-b542928d2356"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Animal Caminando\n",
"Gato Caminando\n"
]
}
],
"source": [
"class AnimalTerrestre:\n",
" def caminar(self):\n",
" print(\"Animal Caminando\")\n",
"\n",
"class Perro(AnimalTerrestre):\n",
" pass\n",
"\n",
"class Gato(AnimalTerrestre):\n",
" def caminar(self):\n",
" print(\"Gato Caminando\")\n",
"\n",
" \n",
"perro = Perro()\n",
"perro.caminar()\n",
"gato = Gato()\n",
"gato.caminar()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "C6N_j9pJDgnv"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qlRkoO7q3HRu"
},
"source": [
"# Herencia multiple\n",
"\n",
"Python soporta la herencia multiple, lo cual nos permite reutilizar código de todas las clases de las cuales se herede "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "6hVYscxA_BbX",
"outputId": "a49db39c-f8ef-4079-eed9-563fa88a50d0"
},
"outputs": [
{
"data": {
"image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" contentStyleType=\"text/css\" height=\"170px\" preserveAspectRatio=\"none\" style=\"width:322px;height:170px;background:#FFFFFF;\" version=\"1.1\" viewBox=\"0 0 322 170\" width=\"322px\" zoomAndPan=\"magnify\"><defs/><g><!--MD5=[d4810177e0cd78618d5cf804f1eed890]\nclass MiSuperClase1--><g id=\"elem_MiSuperClase1\"><rect fill=\"#F1F1F1\" height=\"48\" id=\"MiSuperClase1\" rx=\"2.5\" ry=\"2.5\" style=\"stroke:#181818;stroke-width:0.5;\" width=\"137\" x=\"7\" y=\"7\"/><ellipse cx=\"22\" cy=\"23\" fill=\"#ADD1B2\" rx=\"11\" ry=\"11\" style=\"stroke:#181818;stroke-width:1.0;\"/><path d=\"M24.3438,18.6719 C23.4063,18.2344 22.8125,18.0938 21.9375,18.0938 C19.3125,18.0938 17.3125,20.1719 17.3125,22.8906 L17.3125,24.0156 C17.3125,26.5938 19.4219,28.4844 22.3125,28.4844 C23.5313,28.4844 24.6875,28.1875 25.4375,27.6406 C26.0156,27.2344 26.3438,26.7813 26.3438,26.3906 C26.3438,25.9375 25.9531,25.5469 25.4844,25.5469 C25.2656,25.5469 25.0625,25.625 24.875,25.8125 C24.4219,26.2969 24.4219,26.2969 24.2344,26.3906 C23.8125,26.6563 23.125,26.7813 22.3594,26.7813 C20.3125,26.7813 19.0156,25.6875 19.0156,23.9844 L19.0156,22.8906 C19.0156,21.1094 20.2656,19.7969 22,19.7969 C22.5781,19.7969 23.1875,19.9531 23.6563,20.2031 C24.1406,20.4844 24.3125,20.7031 24.4063,21.1094 C24.4688,21.5156 24.5,21.6406 24.6406,21.7656 C24.7813,21.9063 25.0156,22.0156 25.2344,22.0156 C25.5,22.0156 25.7656,21.875 25.9375,21.6563 C26.0469,21.5 26.0781,21.3125 26.0781,20.8906 L26.0781,19.4688 C26.0781,19.0313 26.0625,18.9063 25.9688,18.75 C25.8125,18.4844 25.5313,18.3438 25.2344,18.3438 C24.9375,18.3438 24.7344,18.4375 24.5156,18.75 L24.3438,18.6719 Z \" fill=\"#000000\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"105\" x=\"36\" y=\"27.8467\">MiSuperClase1</text><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"8\" x2=\"143\" y1=\"39\" y2=\"39\"/><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"8\" x2=\"143\" y1=\"47\" y2=\"47\"/></g><!--MD5=[ae7082bb1f0a94382099e0a7c594e576]\nclass MiClase--><g id=\"elem_MiClase\"><rect fill=\"#F1F1F1\" height=\"48\" id=\"MiClase\" rx=\"2.5\" ry=\"2.5\" style=\"stroke:#181818;stroke-width:0.5;\" width=\"86\" x=\"118.5\" y=\"115\"/><ellipse cx=\"133.5\" cy=\"131\" fill=\"#ADD1B2\" rx=\"11\" ry=\"11\" style=\"stroke:#181818;stroke-width:1.0;\"/><path d=\"M135.8438,126.6719 C134.9063,126.2344 134.3125,126.0938 133.4375,126.0938 C130.8125,126.0938 128.8125,128.1719 128.8125,130.8906 L128.8125,132.0156 C128.8125,134.5938 130.9219,136.4844 133.8125,136.4844 C135.0313,136.4844 136.1875,136.1875 136.9375,135.6406 C137.5156,135.2344 137.8438,134.7813 137.8438,134.3906 C137.8438,133.9375 137.4531,133.5469 136.9844,133.5469 C136.7656,133.5469 136.5625,133.625 136.375,133.8125 C135.9219,134.2969 135.9219,134.2969 135.7344,134.3906 C135.3125,134.6563 134.625,134.7813 133.8594,134.7813 C131.8125,134.7813 130.5156,133.6875 130.5156,131.9844 L130.5156,130.8906 C130.5156,129.1094 131.7656,127.7969 133.5,127.7969 C134.0781,127.7969 134.6875,127.9531 135.1563,128.2031 C135.6406,128.4844 135.8125,128.7031 135.9063,129.1094 C135.9688,129.5156 136,129.6406 136.1406,129.7656 C136.2813,129.9063 136.5156,130.0156 136.7344,130.0156 C137,130.0156 137.2656,129.875 137.4375,129.6563 C137.5469,129.5 137.5781,129.3125 137.5781,128.8906 L137.5781,127.4688 C137.5781,127.0313 137.5625,126.9063 137.4688,126.75 C137.3125,126.4844 137.0313,126.3438 136.7344,126.3438 C136.4375,126.3438 136.2344,126.4375 136.0156,126.75 L135.8438,126.6719 Z \" fill=\"#000000\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"54\" x=\"147.5\" y=\"135.8467\">MiClase</text><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"119.5\" x2=\"203.5\" y1=\"147\" y2=\"147\"/><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"119.5\" x2=\"203.5\" y1=\"155\" y2=\"155\"/></g><!--MD5=[f572b088d68f2e565ab338f074a0f1d5]\nclass MiSuperClase2--><g id=\"elem_MiSuperClase2\"><rect fill=\"#F1F1F1\" height=\"48\" id=\"MiSuperClase2\" rx=\"2.5\" ry=\"2.5\" style=\"stroke:#181818;stroke-width:0.5;\" width=\"136\" x=\"179.5\" y=\"7\"/><ellipse cx=\"194.5\" cy=\"23\" fill=\"#ADD1B2\" rx=\"11\" ry=\"11\" style=\"stroke:#181818;stroke-width:1.0;\"/><path d=\"M196.8438,18.6719 C195.9063,18.2344 195.3125,18.0938 194.4375,18.0938 C191.8125,18.0938 189.8125,20.1719 189.8125,22.8906 L189.8125,24.0156 C189.8125,26.5938 191.9219,28.4844 194.8125,28.4844 C196.0313,28.4844 197.1875,28.1875 197.9375,27.6406 C198.5156,27.2344 198.8438,26.7813 198.8438,26.3906 C198.8438,25.9375 198.4531,25.5469 197.9844,25.5469 C197.7656,25.5469 197.5625,25.625 197.375,25.8125 C196.9219,26.2969 196.9219,26.2969 196.7344,26.3906 C196.3125,26.6563 195.625,26.7813 194.8594,26.7813 C192.8125,26.7813 191.5156,25.6875 191.5156,23.9844 L191.5156,22.8906 C191.5156,21.1094 192.7656,19.7969 194.5,19.7969 C195.0781,19.7969 195.6875,19.9531 196.1563,20.2031 C196.6406,20.4844 196.8125,20.7031 196.9063,21.1094 C196.9688,21.5156 197,21.6406 197.1406,21.7656 C197.2813,21.9063 197.5156,22.0156 197.7344,22.0156 C198,22.0156 198.2656,21.875 198.4375,21.6563 C198.5469,21.5 198.5781,21.3125 198.5781,20.8906 L198.5781,19.4688 C198.5781,19.0313 198.5625,18.9063 198.4688,18.75 C198.3125,18.4844 198.0313,18.3438 197.7344,18.3438 C197.4375,18.3438 197.2344,18.4375 197.0156,18.75 L196.8438,18.6719 Z \" fill=\"#000000\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"104\" x=\"208.5\" y=\"27.8467\">MiSuperClase2</text><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"180.5\" x2=\"314.5\" y1=\"39\" y2=\"39\"/><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"180.5\" x2=\"314.5\" y1=\"47\" y2=\"47\"/></g><!--MD5=[0654f17fc7c81091c41c18896daade65]\nreverse link MiSuperClase1 to MiClase--><g id=\"link_MiSuperClase1_MiClase\"><path codeLine=\"1\" d=\"M106.8998,70.7021 C118.864,85.4485 132.123,101.7909 142.579,114.6784 \" fill=\"none\" id=\"MiSuperClase1-backto-MiClase\" style=\"stroke:#181818;stroke-width:1.0;\"/><polygon fill=\"none\" points=\"101.3256,74.942,94.1607,55,112.198,66.1214,101.3256,74.942\" style=\"stroke:#181818;stroke-width:1.0;\"/></g><!--MD5=[2041990ce8b5b900da124a09536a1333]\nreverse link MiSuperClase2 to MiClase--><g id=\"link_MiSuperClase2_MiClase\"><path codeLine=\"2\" d=\"M216.1,70.7021 C204.136,85.4485 190.877,101.7909 180.421,114.6784 \" fill=\"none\" id=\"MiSuperClase2-backto-MiClase\" style=\"stroke:#181818;stroke-width:1.0;\"/><polygon fill=\"none\" points=\"210.802,66.1214,228.839,55,221.674,74.942,210.802,66.1214\" style=\"stroke:#181818;stroke-width:1.0;\"/></g><!--MD5=[1abc2e5e2d071e577c917d4e08672f35]\n@startuml\nMiSuperClase1 <|- - MiClase\nMiSuperClase2 <|- - MiClase\n@enduml\n\nPlantUML version 1.2022.7beta2(Unknown compile time)\n(GPL source distribution)\nJava Runtime: Java(TM) SE Runtime Environment\nJVM: Java HotSpot(TM) 64-Bit Server VM\nDefault Encoding: UTF-8\nLanguage: en\nCountry: US\n--></g></svg>",
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%plantuml\n",
"\n",
"@startuml\n",
"MiSuperClase1 <|-- MiClase\n",
"MiSuperClase2 <|-- MiClase\n",
"@enduml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Gq7sFdAy3HRu"
},
"outputs": [],
"source": [
"class MiSuperClase1():\n",
" \n",
" def metodo_1(self):\n",
" print(\"metodo_1 metodo llamado\")\n",
" \n",
"class MiSuperClase2():\n",
" \n",
" def metodo_2(self):\n",
" print(\"metodo_2 metodo llamado\")\n",
"\n",
"# Clase que hereda las 2 clases anteriormente definidas\n",
"class MiClase(MiSuperClase1, MiSuperClase2):\n",
" \n",
" def metodo_3(self):\n",
" print(\"metodo_3 metodo llamado\")\n",
"\n",
"# Se crea el objeto \"c\" y luego se llama al metodo_1 y metodo_2 heredados de las 2 primeras clases\n",
"c = MiClase()\n",
"c.metodo_1()\n",
"c.metodo_2()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y52LK2VO3HRu"
},
"source": [
"# El problema del diamante\n",
"\n",
"Cuando tenemos herencia multiple puede suceder que heredemos de una clase la cual extiende de una base común, esto es lo que se llamaria el problema del diamante, y en python se resuelve escogiendo bien el orden en el cual heredamos los elementos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%plantuml\n",
"\n",
"@startuml\n",
"A <|-- B\n",
"A <|-- C\n",
"C <|-- D\n",
"B <|-- D\n",
"@enduml\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Seltml5z3HRu",
"outputId": "89fb45aa-d9b0-44c8-b849-03be36e686fa"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I am the display of Class B\n"
]
}
],
"source": [
"class A:\n",
" def display(self):\n",
" print(\"I am the display of Class A\")\n",
" \n",
" \n",
"class B(A):\n",
" def display(self):\n",
" print(\"I am the display of Class B\")\n",
" \n",
" \n",
"class C(A):\n",
" def display(self):\n",
" print(\"I am the display of Class C\")\n",
" \n",
" \n",
"class D(B, C):\n",
" pass\n",
" \n",
" \n",
"o = D()\n",
"o.display()\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0Bn2K3-V3HRu"
},
"source": [
"# Duck typing\n",
"\n",
"Algo muy interesante de python es la filosofia de duck typing, si camina como pato, parece un pato, entonces es un pato"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "I62FJiG6Dgnw",
"outputId": "eb75469c-bdf6-4aa4-c95a-73fcb66d5572"
},
"outputs": [
{
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C24.9219,150.2969 24.9219,150.2969 24.7344,150.3906 C24.3125,150.6563 23.625,150.7813 22.8594,150.7813 C20.8125,150.7813 19.5156,149.6875 19.5156,147.9844 L19.5156,146.8906 C19.5156,145.1094 20.7656,143.7969 22.5,143.7969 C23.0781,143.7969 23.6875,143.9531 24.1563,144.2031 C24.6406,144.4844 24.8125,144.7031 24.9063,145.1094 C24.9688,145.5156 25,145.6406 25.1406,145.7656 C25.2813,145.9063 25.5156,146.0156 25.7344,146.0156 C26,146.0156 26.2656,145.875 26.4375,145.6563 C26.5469,145.5 26.5781,145.3125 26.5781,144.8906 L26.5781,143.4688 C26.5781,143.0313 26.5625,142.9063 26.4688,142.75 C26.3125,142.4844 26.0313,142.3438 25.7344,142.3438 C25.4375,142.3438 25.2344,142.4375 25.0156,142.75 L24.8438,142.6719 Z \" fill=\"#000000\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"28\" x=\"36.5\" y=\"151.8467\">Fish</text><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"8.5\" x2=\"66.5\" y1=\"163\" y2=\"163\"/><line style=\"stroke:#181818;stroke-width:0.5;\" x1=\"8.5\" x2=\"66.5\" y1=\"171\" y2=\"171\"/><text fill=\"#000000\" font-family=\"sans-serif\" font-size=\"14\" lengthAdjust=\"spacing\" textLength=\"46\" x=\"13.5\" y=\"187.9951\">swim()</text></g><!--MD5=[761036587c7967366c367a2563f1ed62]\n@startuml\nBird : fly()\nAirplane : fly()\nFish : swim()\n@enduml\n\nPlantUML version 1.2022.7beta2(Unknown compile time)\n(GPL source distribution)\nJava Runtime: Java(TM) SE Runtime Environment\nJVM: Java HotSpot(TM) 64-Bit Server VM\nDefault Encoding: UTF-8\nLanguage: en\nCountry: US\n--></g></svg>",
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%plantuml\n",
"\n",
"@startuml\n",
"Bird : fly()\n",
"Airplane : fly()\n",
"Fish : swim()\n",
"@enduml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ix30-m4h3HRu",
"outputId": "07d9b6ed-639f-40cd-8259-98bd1bbcf05b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fly with wings\n",
"fly with fuel\n"
]
},
{
"ename": "AttributeError",
"evalue": "'Fish' object has no attribute 'fly'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32md:\\intro python\\poo_en_python_with_plant.ipynb Cell 39'\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/intro%20python/poo_en_python_with_plant.ipynb#ch0000034?line=10'>11</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mfish swim in sea\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/intro%20python/poo_en_python_with_plant.ipynb#ch0000034?line=12'>13</a>\u001b[0m \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m Bird(), Airplane(), Fish():\n\u001b[1;32m---> <a href='vscode-notebook-cell:/d%3A/intro%20python/poo_en_python_with_plant.ipynb#ch0000034?line=13'>14</a>\u001b[0m obj\u001b[39m.\u001b[39;49mfly()\n",
"\u001b[1;31mAttributeError\u001b[0m: 'Fish' object has no attribute 'fly'"
]
}
],
"source": [
"class Bird:\n",
" def fly(self):\n",
" print(\"fly with wings\")\n",
" \n",
"class Airplane:\n",
" def fly(self):\n",
" print(\"fly with fuel\")\n",
" \n",
"class Fish:\n",
" def swim(self):\n",
" print(\"fish swim in sea\")\n",
"\n",
"for obj in Bird(), Airplane(), Fish():\n",
" obj.fly()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iZxnwV0q3HRv"
},
"source": [
"## Como importamos dependencias externas\n",
"\n",
"No se pierdan la sesión 3 y la ultima de esta serie de tutoriales\n",
"\n",
"Vamos a hablar de como creamos un proyecto trabajando con ficheros individuales mostrando como consumir un API en python usando requests"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kpxu7DXn3HRv"
},
"source": [
"# Conclusión\n",
"\n",
"Python es un lenguaje bastante potente en el cual puedes realizar entre otras cosas automatizaciones de tareas repetitivas, o bien practicar tu lógica en programación, de hecho seria interesante si en este ultimo bloque escribes una funcion que te diga si una palabra es palidroma o no, (se lee igual si escribes los caracteres al reves), y un conversor de temperaturas"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ag18cElp3HRv"
},
"source": [
"DataCamp es una plataforma que sirve para aprender a programar (está en ingles)echa\n",
"https://www.datacamp.com/courses/intro-to-python-for-data-science\n",
"\n",
"Refactoring guru es un sitio donde se pueden encontrar ejemplos de como implementar patrones de código en distintos lenguajes de programación\n",
"https://refactoring.guru/es/design-patterns/python\n",
"\n",
"Curso de python para principiantes de microsoft\n",
"https://docs.microsoft.com/es-es/shows/intro-to-python-development/\n",
"\n",
"Magic methods in python\n",
"https://www.netjstech.com/2019/06/operator-overloading-in-python.html\n",
"\n",
"Clases en python\n",
"https://pythondiario.com/2016/10/herencia-y-polimorfismo-en-python.html#:~:text=La%20herencia%20en%20Python%20nos%20permite%20a%20los,permite%20escribir%20un%20c%C3%B3digo%20m%C3%A1s%20limpio%20y%20legible.\n",
"\n",
"Sobrecargada de operadores\n",
"https://pharos.sh/sobrecargar-funciones-y-operadores-en-python/#:~:text=Sobrecarga%20del%20operador%20Python%20nos%20permite%20cambiar%20el,operadores%20de%20Python%20depende%20de%20las%20clases%20integradas.\n"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "poo_en_python_with_plant.ipynb",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3.10.5 64-bit (windows store)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "9eea658eba0ace8b4a7abaa509b0b66160d954cacf599d4eb5148fe00039b3be"
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