2.Basico/2.1.Estrutura.Geral.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estrutura geral de um Notebook\n",
    "\n",
    "Notebooks possuem célula de Markdown, código e bruta.\n",
    "\n",
    "<style>\n",
    "    .tutorial-text {\n",
    "       color: red;\n",
    "    }\n",
    "</style>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fibonacci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fib(x):\n",
    "    if x <= 1:\n",
    "        return x\n",
    "    return fib(x-1) + fib(x-2)\n",
    "fib(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos **visualizar** os números"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "%matplotlib inline\n",
    "x = range(15)\n",
    "y = [fib(n) for n in x]\n",
    "pyplot.plot(x, y);"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Esta célula **não** é nem Markdown nem Código"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusão\n",
    "\n",
    "Este notebook apresentou brevemente os tipos de células que existem em notebooks.\n",
    "\n",
    "O próximo notebook ([2.2.Estudo.de.Caso.ipynb](2.2.Estudo.de.Caso.ipynb)) apresenta o estudo de caso que usaremmos neste tutorial.\n"
   ]
  }
 ],
 "metadata": {
  "julynter-results": {
   "filteredId": [],
   "filteredIndividual": [],
   "filteredRestart": [],
   "filteredType": [],
   "hash": "9e088ce637e8038d85778693dab25e54a66a64a5",
   "visible": [
    {
     "cellId": "group",
     "hash": "d6fbc9d2bdd580e18ed0bc5805dc26db323d6f5f",
     "reason": "This groups other lint messages",
     "reportId": "group",
     "reportType": "import",
     "suggestion": null,
     "text": "Import"
    },
    {
     "cellId": 9,
     "hash": "4a1687404f6b8df1d133a3a0ba9941731d0f9feb",
     "reason": "Imports at the beginning of the notebook allow for a quick failure in the case of a non-installed dependency. It prevents users from stopping their executions for installing dependencies. Additionally, imports in the middle might reduce notebooks' readability by taking the attention from the logic to the import constructs.",
     "reportId": "i1",
     "reportType": "import",
     "suggestion": "Please consider moving the import to the first cell of the notebook.",
     "text": "Cell 9 has imports but it is not the first cell"
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}