2017-07-06_Jupyter_101_GruPy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Um texto de explicação\n",
    "\n",
    "- m: markdown\n",
    "- y: código\n",
    "\n",
    "- a: cria bloco antes\n",
    "- b: cria bloco depois\n",
    "- x: elimina bloco\n",
    "\n",
    "- Ctrl + Enter: executa o bloco\n",
    "- Shift + Enter: executa o bloco e muda o cursor para o próximo\n",
    "\n",
    "- Ctrl + Shift + \"-\": Quebra o bloco \"no meio\", a partir do lugar selecionado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custo = 2+2*3\n",
    "pagto = 15\n",
    "lucro = pagto - custo\n",
    "lucro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Ativa os plots para serem visualizados aqui!\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mR2IC/zO6DzcN6Uz7Fo0iXaKI1DAFQC23Jfcwb6zK4Y3Vu9lVcJymDetx45CO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\nP6MiCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6844a58a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2, 2, 200) ; y = x ** 2 + 5 * x - 2\n",
    "# Usando o ponto-e-vírgula apenas para não\n",
    "# mostrar o valor devolvido pela função\n",
    "plt.plot(x, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos colocar LaTeX com \\$\\$:\n",
    "\n",
    "$$\n",
    "f(x) = x^2 + 5 x - 2\n",
    "$$\n",
    "\n",
    "Pode-se colocar também inline: $f(3) = 3^2 + 5\\cdot3 - 2$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O \"require\" é um comando não-padrão do LaTeX, usado aqui como alternativa ao \"usepackage\", porém dentro da expressão.\n",
    "\n",
    "$$\\require{cancel}\n",
    "2 + \\cancel{5} = x + \\cancel{5}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red>Tem alguém da Red Hat aqui!</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lição de casa: ver se é possível integrar JavaScript com o MarkDown!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from shapely.geometry import * # Foi só porque pediram!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"0.92 0.92 1.1600000000000001 2.16\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,4.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.0432\" opacity=\"0.6\" d=\"M 1.0,1.0 L 1.0,3.0 L 2.0,3.0 L 1.0,1.0 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7f6844081748>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Polygon([(1, 1), (1, 3), (2, 3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a    b\n",
       "0  2  4.0\n",
       "1  5  1.0\n",
       "2  3  NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.DataFrame({\"a\": [2, 5, 3], \"b\": [4, 1, np.nan]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it!! =D\n",
    "\n",
    "By Danilo J. S. Bellini\n",
    "(com ajuda da platéia!)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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