00 - GUIA PARA LA CREACION DE NOTEBOOKS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Guía general para los notebooks del curso\n",
    "\n",
    "Se sugiere que los notebooks del repositorio tengan sigan la siguiente estructura\n",
    "\n",
    "- Los notebooks se numeran `00 -`, `01 -`, etc. como caracteres iniciales de su propio nombre, o con algún esquema numérico de modo que queden ordenados al listarlos.\n",
    "- Los nombres de los notebooks serán en mayúscula.\n",
    "- Habrá dos tipos: **notas explicativas** y **talleres**.\n",
    "- Los notebooks de talleres contienen `[TALLER]` en el nombre. P.ej. `06 - [TALLER] - ALGORITMOS DE BUSQUEDA`\n",
    "\n",
    "Para **garantizar la ejecución de los notebooks en Google Colab**:\n",
    "\n",
    "- Todos los recursos requeridos (imágenes, librerías, datos, etc.) deben de colocarse en la carpeta `local`\n",
    "- Se debe de seguir la convención ilustrada más abajo para incluir imágenes y usar librerías y datos contenidos en `local`.\n",
    "- Especialmente las **imágenes** han de ser incluidas como elementos ejecutables `Image` (no como elementos HTML en celdas markdown)\n",
    "- Se debe de incluir la siguiente celda al prinipio de notebook. Esto garantiza que cuando se ejecute en cualquier entorno (incluido Google Colab), se descarguen localmente los recursos del repositorio si se necesitan.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget --no-cache -O init.py -q https://raw.githubusercontent.com/UDEA-Esp-Analitica-y-Ciencia-de-Datos/EACD-01-FUNDAMENTOS/master/init.py\n",
    "import init; init.init(force_download=False); \n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"local/imgs/udea-datascience.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTING A LIBRARY**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a demo function\n"
     ]
    }
   ],
   "source": [
    "from local.lib import demo\n",
    "demo.demofunc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**USING LOCAL DATA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "d = pd.read_csv(\"local/data/demodata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "d\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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