Aula0 - Introducao-Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introdução à linguagem Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Francisco Aparecido Rodrigues, francisco@icmc.usp.br.<br>\n",
    "Universidade de São Paulo, São Carlos, Brasil.<br>\n",
    "https://sites.icmc.usp.br/francisco <br>\n",
    "Copyright: Creative Commons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nesta aula, vamos conhecer a linguagem de programação Python. Não precisamos entrar nos detalhes da linguagem, mas aprender as principais funcionalidades que são necessárias para processamento de dados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para inicarmos a programação em Python, precisamos fazer a sua instalação. Os detalhe sobre a instalação dependem do sistema operacional que está sendo usado. Na página oficial sobre a linguagem Python há um tutorial sobre a instalação: https://wiki.python.org/moin/BeginnersGuide/Download"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A página oficial da linguagem também oferece muita informação sobre comandos e conceitos e também diversos tutoriais: https://www.python.org/about/gettingstarted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma boa alternativa para começar a pogramar em Python é considerar plataformas como Anaconda: https://www.anaconda.com/download/#macos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nessa plataforma, temos o Jupyter Notebook, que é o editor usado para preparar esse material. Para executar os comandos abaixo, vocês podem usar esse editor ou o Spyder, copiando e colando os comandos no editor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0 Reiniciando variáveis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um aspecto importante da programação em Python é ter controle sobre as variáveis inicializadas. Por isso, é sempre importante reiniciar todas as variáveis sempre que um program em Python for implementado. Para \"limpar\" as variáveis, usamos os comandos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Limpando as variaveis\n",
    "from IPython import get_ipython\n",
    "get_ipython().magic('reset -sf') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note que se chamarmos esses comandos após a inicalização, haverá uma mensagem de erro:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x =  [1, 2, 3]\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'x' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-4ad2dd206092>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'reset -sf'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Note que um erro será mostrado, pois x foi reiniciada.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'x' is not defined"
     ]
    }
   ],
   "source": [
    "x = [1,2,3]\n",
    "print(\"x = \", x)\n",
    "\n",
    "# Limpando as variaveis\n",
    "from IPython import get_ipython\n",
    "get_ipython().magic('reset -sf') \n",
    "\n",
    "print(\"x = \", x) # Note que um erro será mostrado, pois x foi reiniciada."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Tipos de dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A linguagem Python suporta diversos tipos de dados, como int, float e strings. Para varíaveis do tipo inteiro:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "a = 2\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Float:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5\n"
     ]
    }
   ],
   "source": [
    "a = 2.5\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Também podemos converter de float para inteiro:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "a = int(3/2)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Números complexos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.5+0.5j)\n"
     ]
    }
   ],
   "source": [
    "a=1.5+0.5j\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parte real: 1.5 Parte imaginária: 0.5\n"
     ]
    }
   ],
   "source": [
    "print('Parte real:', a.real, 'Parte imaginária:', a.imag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tipos booleanos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a =  False\n",
      "b =  True\n",
      "c =  False\n"
     ]
    }
   ],
   "source": [
    "a = 3 > 4\n",
    "print('a = ', a)\n",
    "b = True\n",
    "print('b = ', b)\n",
    "c = False\n",
    "print('c = ', c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para determinar o tipo de uma variável:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tipo de a: <class 'float'>\n",
      "Tipo de b: <class 'bool'>\n"
     ]
    }
   ],
   "source": [
    "a = 3.5\n",
    "print('Tipo de a:', type(a))\n",
    "b = False\n",
    "print('Tipo de b:', type(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para somar duas variáveis escalares:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z =  3\n"
     ]
    }
   ],
   "source": [
    "x = 1\n",
    "y = 2\n",
    "z = x + y\n",
    "print(\"z = \", z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiplicação e potenciação:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a*b =  4.6\n",
      "a^3 =  8\n"
     ]
    }
   ],
   "source": [
    "a = 2\n",
    "b = 2.3\n",
    "print(\"a*b = \", a*b)\n",
    "print('a^3 = ', a**3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para determinarmos o resto de uma divisão:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "a = 5%2\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Listas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma lista é um conjunto de dados onde os elementos não precisam ser do mesmo tipo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 'a', 'palavra', (1.5+0.5j)]\n"
     ]
    }
   ],
   "source": [
    "L = [1, 2, 'a', 'palavra', 1.5+0.5j]\n",
    "print(L)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para acessar um elemento da lista:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 a\n"
     ]
    }
   ],
   "source": [
    "L = [1, 2, 'a', 'palavra', 2.5, 1.5+0.5j]\n",
    "print(L[0], L[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Índices negativos são usados para acessar do final para o começo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.5+0.5j) 2.5 palavra\n"
     ]
    }
   ],
   "source": [
    "L = [1, 2, 'a', 'palavra', 2.5, 1.5+0.5j]\n",
    "print(L[-1], L[-2], L[-3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos acessar vários elementos de uma vez:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'l' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-0226f3752f2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'palavra'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.5\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m0.5j\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'l' is not defined"
     ]
    }
   ],
   "source": [
    "L = [1, 2, 'a', 'palavra', 1.5+0.5j]\n",
    "print(l[0:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para adicionar um novo elemento na lista:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = [1, 2, 'a', 'palavra', 2.5, 1.5+0.5j]\n",
    "L.append('Novo elemento')\n",
    "print(L)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para remover o último elemento:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = [1, 2, 'a', 'palavra', 2.5, 1.5+0.5j]\n",
    "L.pop()\n",
    "print(L)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para inverter a ordem dos elementos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = [1, 2, 'a', 'palavra', 2.5, 1.5+0.5j]\n",
    "L = l[::-1]\n",
    "print(L)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para concatenar uma lista:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = [1, 2, 'a', 'palavra', 2.5, 1.5+0.5j]\n",
    "k = [0, 9, 3, 'mundo']\n",
    "m = L + k\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para repetir os elementos uma lista:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = [1, 2, 'a', 'palavra', 2.5, 1.5+0.5j]\n",
    "k = 2*L\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ordenando uma lista:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = [4,3,1,5,6,8,9]\n",
    "L.sort()\n",
    "print(L)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note que a soma dos elementos de listas não é feita de forma direta:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1,2,3]\n",
    "y = [4,5,6]\n",
    "z = x + y\n",
    "print('z = ', z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para somar os elementos de uma lista, podemos usar a biblioteca numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = [1,2,3]\n",
    "y = [4,5,6]\n",
    "z = np.add(x, y)\n",
    "print('z = ', z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outra maneira de realizar essa soma é converter as listas para vetores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1,2,3]\n",
    "x = np.array(x)\n",
    "y = [4,5,6]\n",
    "y = np.array(y)\n",
    "z = x + y\n",
    "print('z = ', z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Vetores e matrizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vetores e matrizes não são tipos de dados padrões da linguagem Python. Para trabalharmos com dados numéricos, podemos usar a biblioteca Numpy, que é uma biblioteca científica para cálculo de vetores multidimensionais. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para maiores informações sobre essa biblioteca, vejam: https://numpy.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As bibliotecas Numpy, SciPy e Matplotlib podem ser usadas de forma equivalente ao Matlab, possuindo funções bastante parecidas. Logo, para usurários de Matlab, a migração para Python é bastante simples. A linguamge Python também apresenta similaridades com a linguagem R. No entanto, C, C++, Java são bastante diferentes de Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para definirmos dois vetores e realizamos a soma:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # os comandos da biblioteca numpy serao precedidos por 'np.'\n",
    "\n",
    "x = np.array([1,5,2])\n",
    "y = np.array([7,4,1])\n",
    "z = x + y\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para iniciar um vetor com elementos iguais a zero ou um, usamos os seguintes comandos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "N = 3\n",
    "x = np.zeros(N)\n",
    "y = np.ones(N)\n",
    "print(\"x = \", x, \"y = \", y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para sabermos o tamanho de um vetor, usamos o comando:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1,2,3]\n",
    "x = np.array(x)\n",
    "N = np.shape(x)[0]\n",
    "print(\"N = \", N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O produto escalar de dois vetores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1,2,3])\n",
    "y = np.array([1,4,6])\n",
    "z = np.dot(x,y)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "E o produto vetorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([0,0,1])\n",
    "y = np.array([0,1,0])\n",
    "z = np.cross(x,y)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Além de vetores, podemos definir variáveis como matrizes. Uma maneira é considerar uma lista de listas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = [[1,2,3], [4,5,6], [7,8,9]]\n",
    "print(\"A = \", A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nesse caso, deve-se atentar para a operação \"+\", que não representa a soma dos elementos, mas uma união de listas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = [[1,2,3], [4,5,6], [7,8,9]]\n",
    "B = [[-1,-2,-3], [1,1,1], [0,0,0]]\n",
    "C = A + B\n",
    "print(\"C = \", C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para usar matrizes de maneira usual, é mais adequado consideramos a biblioteca Numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.array([[1,2,3], [4,5,6], [7,8,9]])\n",
    "B = np.array([[10,11,12], [13,14,15], [16,17,18]])\n",
    "C = 2*(A + B)\n",
    "print(\"A = \\n\", A)\n",
    "print(\"B = \\n\", B)\n",
    "print(\"C =2*(A + B) = \\n\", C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para saber o número de linhas e colunas em uma matriz, usamos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.array([[1,2,3], [4,5,6]])\n",
    "[nrow, ncol] = np.shape(A)\n",
    "print(\"A = \\n\", A)\n",
    "print(\"Numero de linhas:\", nrow, \"\\nNumero de colunas:\", ncol,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para elevar os elementos de um vetor a uma data potência:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1,2,3])\n",
    "y = x**3\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Também podemos usar a função pow():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 3\n",
    "x = np.array([1,2,3])\n",
    "y = pow(x,n)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Strings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para definirmos uma string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'Hello world'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma string é intepretada como uma lista de caracteres:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(s[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos realizar operações em strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'Hello'\n",
    "t = 'World'\n",
    "v = s + t\n",
    "print(v)\n",
    "print(s + '\\t' + t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'A'\n",
    "t = 4*s\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos procurar uma palavra dentro de uma frase com operador 'in':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'Mundo'\n",
    "s in 'Mundo, mundo vasto mundo.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s in 'A Terra é um planeta azul.'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para sabermos o tamanho de uma string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'Hello'\n",
    "print(len(s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ainda converter números para string com a função 'str':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 2\n",
    "s = 'O número ' + str(x) + ' é par'\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'Pi é igual a ' + str(np.pi)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos acessar apenas alguns dos elementos da string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'ABCDEFGHIJK'\n",
    "print(s[0:8:2]) # comecando na posicao 0 e selecionando de 2 em 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mostrando o valor de uma string (notem o caracter 'f' dentro da função print):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 20\n",
    "m = 25\n",
    "prod = n * m\n",
    "print(f'The product of {n} and {m} is {prod}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos substituir um valor em uma string usando o comando replace:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'Hello world'\n",
    "x = 'Earth'\n",
    "s = s.replace('world', x)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mundando para letras minúsculas ou maíusculas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'HELLo worlD'.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = 'HeLLo world'\n",
    "s.upper()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outros comandos úteis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'este eh um teste'.count('te')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'comando'.endswith('do')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'esta eh uma sentenca'.find('uma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'esta eh uma sentenca'.find('nada')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'O mundo é pequeno.'.replace('pequeno', 'grande')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = list('world')\n",
    "print(l)\n",
    "':'.join('world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'esta eh uma setenca'.rsplit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'www.amazon.com.br'.rsplit(sep='.', maxsplit=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para saber mais sobre strings: https://realpython.com/python-strings/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Dicionários"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dicionários são similares a listas, mas podem usar qualquer tipo imutavel de dados como indice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tel = {'Antonio': 5752, 'Maria': 5578}\n",
    "tel['Paulo'] = 5915\n",
    "print(tel)\n",
    "print(tel['Antonio'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para acessar as chaves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tel.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "E o valores armazenados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tel.values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dicionários podem armazenar tipos diferentes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'a':1, 'b':2, 3:'hello'}\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos realizar operações com dicionários:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "estoque = {'abacaxis': 10, 'bananas': 24, 'laranjas': 36, 'peras': 12}\n",
    "del estoque['peras']\n",
    "print(estoque)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "estoque['abacaxis'] = estoque['abacaxis'] + 10\n",
    "print(estoque)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dicionários podem ser usados para criar histogramas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "letterCounts = {}\n",
    "for letter in \"banana\":\n",
    "    letterCounts[letter] = letterCounts.get(letter,0) + 1\n",
    "print(letterCounts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 2 Estruturas Condicionais"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "O comando if tem a seguinte estrutura:<br>\n",
    "if condition: <br>\n",
    "&emsp; if_body <br>\n",
    "else: <br>\n",
    "   &emsp; else_body"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Note que após a condição, usamos ':' e a linha a seguir deve conter um espaço antes do comando. Por exemplo, para verificarmos se um número é par:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 3\n",
    "if x%2 == 0: # O comando % retorna o resto da divisao\n",
    "    print('x é par.')\n",
    "else:\n",
    "    print('x é ímpar.')   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Além do comando if, podemos usar o comando elif: para combinar condicionais:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 200\n",
    "b = 33\n",
    "if b > a:\n",
    "  print(\"b is greater than a\")\n",
    "elif a == b:\n",
    "  print(\"a and b are equal\")\n",
    "else:\n",
    "  print(\"a is greater than b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Podemos executar o comando em uma única linha:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "if a > b: print(\"a is greater than b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Ou então:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B\n"
     ]
    }
   ],
   "source": [
    "a = 3\n",
    "b = 4\n",
    "print(\"A\") if a > b else print(\"=\") if a == b else print(\"B\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Repetição"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As estruturas de repetição em Python incluem os laços for e while."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O laço for é usado para iterar em uma sequência, que pode ser uma lista, tupla, dicionário, set ou string. A estrutura do loop for:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for iterator: <br>\n",
    "&emsp; commands <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por exemplo, considerando uma lista:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maça\n",
      "banana\n",
      "abacate\n"
     ]
    }
   ],
   "source": [
    "fruits = [\"maça\", \"banana\", \"abacate\"]\n",
    "for x in fruits:\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Em uma string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b\n",
      "a\n",
      "n\n",
      "a\n",
      "n\n",
      "a\n"
     ]
    }
   ],
   "source": [
    "for x in \"banana\":\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para encerrar a execução do laço for, podemos usar o comando break:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maça\n",
      "banana\n"
     ]
    }
   ],
   "source": [
    "fruits = [\"maça\", \"banana\", \"abacate\"]\n",
    "for x in fruits:\n",
    "  print(x) \n",
    "  if x == \"banana\":\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ainda definir o laço em um intervalo definido pela função range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "for x in range(2, 8, 2):\n",
    "  print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos usar o comando else juntamente com o for:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "Finally finished!\n"
     ]
    }
   ],
   "source": [
    "for x in range(6):\n",
    "  print(x)\n",
    "else:\n",
    "  print(\"Finally finished!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Laço podem ser incluídos dentro de outros laços (nested loops):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vermelha maça\n",
      "vermelha banana\n",
      "vermelha abacate\n",
      "grande maça\n",
      "grande banana\n",
      "grande abacate\n",
      "doce maça\n",
      "doce banana\n",
      "doce abacate\n"
     ]
    }
   ],
   "source": [
    "adj = [\"vermelha\", \"grande\", \"doce\"]\n",
    "fruits = [\"maça\", \"banana\", \"abacate\"]\n",
    "\n",
    "for x in adj:\n",
    "  for y in fruits:\n",
    "    print(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "No caso do laço while, a sequência de comandos é repetida equanto a condição for verdadeira:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "while condition: <br>\n",
    "&emsp; commands <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "while i < 6:\n",
    "  print(i)\n",
    "  i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos interromper o laço com o comando break:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "while i < 6:\n",
    "  print(i)\n",
    "  if i == 3:\n",
    "    break\n",
    "  i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Funções"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Uma função é um bloco de comando que é executado quando a função é chamada. A estrutura de uma função:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "def functionname( parameters ):<br>\n",
    "&emsp; commands <br>\n",
    "&emsp; return [expression]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Funções são úteis para:\n",
    "- Dar um nome para uma seqüência de comandos \n",
    "- Tornar o programa mais fácil de ler e de depurar.\n",
    "- Dividir um programa longo em funções permite que você separe partes do programa, depure-as isoladamente, e então as componha em um todo.\n",
    "- Funções facilitam tanto recursão quanto iteração.\n",
    "- Funções bem projetadas são freqüentemente úteis para muitos programas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eu nasci em Espanha\n",
      "Eu nasci em México\n",
      "Eu nasci em Portugal\n",
      "Eu nasci em Brazil\n"
     ]
    }
   ],
   "source": [
    "def my_function(country = \"Brazil\"):\n",
    "  print(\"Eu nasci em \" + country)\n",
    "\n",
    "my_function(\"Espanha\")\n",
    "my_function(\"México\")\n",
    "my_function(\"Portugal\")\n",
    "my_function()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma função pode retornar valores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n",
      "25\n",
      "45\n"
     ]
    }
   ],
   "source": [
    "def my_function(x):\n",
    "  return 5 * x\n",
    "\n",
    "print(my_function(3))\n",
    "print(my_function(5))\n",
    "print(my_function(9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Em Python, os parâmetros de uma função podem ser passados por valor ou referência. Vejam o exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Antes: x: [1, 2, 3] y: [0, 0]\n",
      "Depois: x: [1, 2, 3, [0, 0, 0]] y: [0, 0]\n"
     ]
    }
   ],
   "source": [
    "def change(x, y):\n",
    "    x.append([0,0,0])\n",
    "    y = [-1,-1]\n",
    "    return\n",
    "\n",
    "x = [1,2,3]\n",
    "y = [0,0]\n",
    "print(\"Antes:\", 'x:', x, 'y:', y)\n",
    "change(x,y)\n",
    "print(\"Depois:\", 'x:', x, 'y:', y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Note que quando usamos funções, devemos definir variáveis como globais ou locais. Por exemplo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inside the function local total :  30\n",
      "Outside the function global total : 0\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/python\n",
    "total = 0; # This is global variable.\n",
    "# Function definition is here\n",
    "def sum( arg1, arg2 ):\n",
    "   # Add both the parameters and return them.\"\n",
    "   total = arg1 + arg2; # Here total is local variable.\n",
    "   print(\"Inside the function local total : \", total)\n",
    "   return total;\n",
    "\n",
    "# Now you can call sum function\n",
    "a = sum( 10, 20 );\n",
    "print(\"Outside the function global total :\", total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 Arquivos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Abrindo um arquivo para escrita:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "myfile = open('myfile.txt', 'w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Escreve uma linha no arquivo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfile.write('hello text file\\n')\n",
    "myfile.write('goodbye text file\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, Y, Z = 43, 44, 45\n",
    "myfile.write('%s,%s,%s\\n' % (X, Y, Z))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fecha o arquivo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "myfile.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abre o arquivo apenas para leitura."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "myfile = open('myfile.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lê a primeira linha do arquivo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello text file\\n'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfile.readline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le a próxima linha:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'goodbye text file\\n'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfile.readline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lê novamente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'43,44,45\\n'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfile.readline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfile.readline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos gerar uma tabela e escrevê-la em um arquivo com um único comando, usando a biblioteca numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.36473304 0.7105385  0.0989745  0.29178012 0.79218091 0.680054\n",
      "  0.26914619 0.23119005 0.48189064 0.02893561]\n",
      " [0.84576465 0.59787426 0.24248844 0.70253641 0.86214836 0.57155949\n",
      "  0.32422614 0.79590467 0.00677766 0.87833813]\n",
      " [0.87197354 0.45486589 0.55931484 0.4288103  0.55387413 0.19905973\n",
      "  0.83199998 0.29319923 0.33387397 0.92801   ]\n",
      " [0.50489646 0.08247445 0.27629866 0.49114568 0.8888069  0.68146999\n",
      "  0.1028501  0.41542689 0.41083159 0.0619841 ]\n",
      " [0.16050859 0.71024347 0.76293193 0.90824755 0.68335623 0.19947192\n",
      "  0.22766846 0.97300128 0.05538928 0.01267458]\n",
      " [0.97445442 0.81212114 0.69951258 0.82474444 0.18999466 0.95613779\n",
      "  0.5703587  0.5439519  0.04219734 0.72074439]\n",
      " [0.10436599 0.08072406 0.84018471 0.18746287 0.3271151  0.60006002\n",
      "  0.17325608 0.54997627 0.11033272 0.19261624]\n",
      " [0.82818749 0.88012388 0.31598786 0.33919326 0.99867123 0.34779254\n",
      "  0.65427501 0.70203955 0.96048017 0.95621755]\n",
      " [0.01821646 0.46321647 0.62863378 0.99961772 0.22768418 0.87565998\n",
      "  0.31485105 0.40667018 0.99103637 0.62838218]\n",
      " [0.53507314 0.46488753 0.48820562 0.48300831 0.53601502 0.13953418\n",
      "  0.14594391 0.34181645 0.38291341 0.11893614]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.random.rand(10,10)\n",
    "print(x)\n",
    "np.savetxt('dados.txt', x, delimiter=',', fmt='%1.3f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para efetuarmos a leitura:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.365 0.711 0.099 0.292 0.792 0.68  0.269 0.231 0.482 0.029]\n",
      " [0.846 0.598 0.242 0.703 0.862 0.572 0.324 0.796 0.007 0.878]\n",
      " [0.872 0.455 0.559 0.429 0.554 0.199 0.832 0.293 0.334 0.928]\n",
      " [0.505 0.082 0.276 0.491 0.889 0.681 0.103 0.415 0.411 0.062]\n",
      " [0.161 0.71  0.763 0.908 0.683 0.199 0.228 0.973 0.055 0.013]\n",
      " [0.974 0.812 0.7   0.825 0.19  0.956 0.57  0.544 0.042 0.721]\n",
      " [0.104 0.081 0.84  0.187 0.327 0.6   0.173 0.55  0.11  0.193]\n",
      " [0.828 0.88  0.316 0.339 0.999 0.348 0.654 0.702 0.96  0.956]\n",
      " [0.018 0.463 0.629 1.    0.228 0.876 0.315 0.407 0.991 0.628]\n",
      " [0.535 0.465 0.488 0.483 0.536 0.14  0.146 0.342 0.383 0.119]]\n"
     ]
    }
   ],
   "source": [
    "dt = np.loadtxt('dados.txt',delimiter=',')\n",
    "print(dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 Gráficos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Para graficar os dados, usamos a biblioteca Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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52zaXDmPGDHtkN4z6ePfdkEfZsGE5UScmXnw9kwF2BVaE9VcGxupyioh8IiKTRSSYLzfecxGRUSJSJiJla9asSYbcSSXa+n5K1v332gsuucS1Z86E559PwUUMI4tYuRIGDnTt4uLQkpkB+F/JRHqErhtb/W+gs6ruB7wFPJ7AuW5QdaKqlqpqabt27RosbNbwhz+EcpldcQUcemjOGy+jEc3Zwr5GOcTvfhcy9l9zDey+u7fy+Ay/K5mVQFglDzoBq8IPUNW1qrol0H0Q6B3vuUYUdt45VLXv669h1iy45RZvZfIp4bEz//53aPy3v/VOJiNNBAMvp08Pjd10k7n+18HvSmY2sJeIdBGRpsAw4OXwA0SkJKx7IrAg0H4dOFpEWgcM/kcHxjKKoCdZ2rnsslBbFe6/32Jn6uG442DAANe+/35YsCD28UaGU17u7JdBmjbN+cDLSPhayahqFXARTjksAJ5T1c9F5BYROTFw2CUi8rmIfAxcAowMnLsOuBWnqGYDtwSdADKJxx7z6MLl5dC/f6ifn28/oHoQCSXara6Grl0tQDOrUXVJZcH9g6uqcj7wMhJ+9y5DVV8DXqszdmNY+zrguijnPgI8klIBU8iPP8bO8NLg4Mt4KCmBvfeGt992/epqt9kPKCb77Rd9nwVoZhk33uh+EwATJ8JHH7nsy0YtfK9kcpn/+7/Qd/bxx+H3v0+zAJWVzrjw3HPuqS187dkwcpnPPnOlywGOPx7OOSf28TmMKRmfsnq1y1AB0LOnc2BJO8HYmebN4ZFHnMabOdNiAAzjmmtcXrK8PHNZrgdf22RykWDVy5KSkFfkvHke1xK7+WaXahhgzBir0GXkNm+/7coqA5x9tjO+GVExJeMz0hp4GS+dOrkiTAAffJDzuZiMHObrr+Gkk1y7WTP3AGbExJSMER/XXAO77OLaY8a4HGcWoBmRaA4Z7dunVw4jBYwYESqLcdVVbsnBiIkpGSM+WrVymQDAuTHPnGkBmlEID9B87rnQ+KhR3slkNJJg4OXUqaGxW26xuLE4MCXjI3xv6rguzFPcAjTjYsgQV+IdXAyNTf4ylPJyKC0N9QsKLG4sTkzJ+Ig33vBagnqoW1rWfmj1IgJLlrj2pk1udcWCMzOQli1djSVw/7zqagu8jBNzYfYJNTVw/fXR96c08DJeSkqge3d47z3X37bNpdKwH1pMvvsu8rgFZ2YQ994LP/3k2rfd5hwALPAyLkzJ+ITJk2HuXNceO9bl2fMl33wDv/lNyIVz1ixv5TGMVPPdd6GgtW7dnBOMFSSLG1su8wFVVaGKl23buuz6vmXKFHjlldCy2YoV9kRnZDd33QXff+/at99uCiZBTMl4SDDwsqAAFi1yY99+61KG+RoR+NOfXPunn9wPzzCykdWrXSllgIMPhhNO8FaeDMSUjIf4MvAyXg47DI4+2rUnTjTjv5F9VFRA797OYwPcg5WVIk8YUzJGwwnOYLZts8jnGERz2mjaNL1yGAkyZgysCtQ5PPpoOPxwT8XJVEzJGA2ntBQGD3btJ55wASEWCLID4cGZqnDJJW5861aYNs1b2YwIBAMvn3oqNPbGGxYP1kB8r2RE5FgRWSgiS0Tk2gj7rxCR+SLyiYhMFZGfh+2rFpF5ge3luucaSeDWW90PUhXKyiwLQBxcd13ofnXDDRkQhJtrlJe7MqdBrGBfo/C1khGRfGACMBDoCgwXkbopTz8CSlV1P2AycGfYvp9UtWdgOxEfsX691xIkid69a98lLQtAvXTs6DLEA7z/vmtbgKaPKCkJVbwEF8RmgZcNxtdKBugDLFHVclXdCjwLnBR+gKpOU9WAZY4PgE5plrFBjB8ffZ8vAi/jpbwcTgzT33l59tQXB8EyDnXJCKePbOezz2DpUtceOBDOP9+WgRuB34MxdwVWhPVXAgfGOP5s4D9h/SIRKQOqgHGq+mKkk0RkFDAKYPfdd2+UwPGwbl2oFnyvXm6VKWOdVkpKahe7qalxbs321GdkKmPHute8PPc06PuYAn/j95lMpFtvxBVsEfkdUArcFTa8u6qWAqcB40Vkz0jnqupEVS1V1dJ27do1VuZ6uftu+OEH1w6aNDKayko44wxoEnhmmTnTW3kMo6F89FGoXtLvf28KJgn4fSazEtgtrN8JWFX3IBE5ErgBOExVtwTHVXVV4LVcRN4B9geWplLgaHTsGHkp5KyzsmAmHvxR7rQT3HefSz0zaxb07eutXIaRKDfe6F6bNAml4TAahd9nMrOBvUSki4g0BYYBtbzERGR/4AHgRFX9Jmy8tYgUBtptgb7A/LRJXoeMDryMl+uvh8JC1w4uORhGpvDhhy5lEsCZZ8Iee3grT5bgayWjqlXARcDrwALgOVX9XERuEZGgtfkuoAXwfB1X5V8BZSLyMTANZ5PxTMnkBLvuCqNHu/bUqTB9urfy+Jhozh2tWqVXDiOMawMREgUFoQJ9RqMRNSf9WpSWlmpZWVnS3zeW3SWr/gWrV7snwJ9+ggMPdDObSZPMESAGmza5P1llJey1F8yfHzJvGWli1iw45BDX7tYNPv3UW3kyEBGZE7CB18LXMxkjA+nYES680LU//BDefdcCNOuhWbNQ0dHFi2sHmhtpoLg4pGDAuTBbrFfSMCVjJJ+//S3UtjLNcTFqlPMGB+dxuG2bt/LkFE8/XbvfrJnFeiURUzJp4Ouvo+/LqMDLeFm2DLqGJWYoLLQfbT0UF8PGja69dKlLnmlZANJE+ENRYSFs3mwR/knElEwauOOOUPu992onS8x49+VIlJRAnz6h/pYtzr3ZfrQx2bAh8nhWeSD6jenTQ1lK99vPLfGOHp2lP0xvMPNiilm5Eh580LWPOcbVPcoJ1q936QyCNaU/+cRbeQwjEsE658XF8Prr7kFowgRPRco2TMmkmDvucCndIfR9zgmmTIG1a6FzZ7cOFJy6ZXx6AyNreOcdt4HLT2Yz7ZSQ0HKZiOwtIheIyLMi8p6ILBKRj0XkTRG5S0R+IyJFqRI2UwiWVRZxAfBBBg3yTiZP2GWXUPGU9993NTkMwy8EC+0VF8PVV3srSxYTV5yMiAwDLsBFzUPknGLg8op9DzwG/E1VlzdexPSSjDiZnImJiYe1a6FLF2dwOOggZ5Sy2UxE7HuTRl54AYYMce0rr3QJBY1G0aA4GRE5QkQ+Ap4GugGP47IV9wQ6Ak2BVsAewG+AW4GFwOXAAhH5s4i0TOYHMTKM8NnMBx/YbCYG0TwNW9ovKPlcfLF7zc93ZZaNlBFzJiMiNcBc4M/AS4GaLvW/qchewOjANk5Vb02CrGnBZjIpIHw206sXtGhhWQDqYcsWF/2/YoUzay1a5LKdGI2kuNi5KNelqMhlqTAaTEMj/ocEUuA/H6+CAVDVxap6JbAn8GaCshrZRvhsZu5cywIQB4WFLt8owPLl8MQTnoqTPZSXQ/v2oX5xscVwpZiYSkZVpzTmzVV1tap+0Jj3MLKE8DVvywIQF2eeCbsFCl3cdptlAUgKCxe6UhTglsq2bLHAyxSTkmBMEclZ1+iVK6Pvy8ro/nhZtgz23TfUtywA9VJYCN9/79rLl1sWgKQQ9CjLz3f2QQu8TDmJujBPrM9FWUS6ADlbGnHcuFD7/fdzILo/XkpK4IADQn3LAhAXlgUgiUyfHoqLufRS6N/fBV5OadSCjVEPic5kzgH+JyL7RNopIkNwjgIHRNqf7Xz9de3o/oMO8lYe3xHMAhDE0qkb6SQ4iykqMo+yNJKokrkd6IorBnZmcFBEmorIfcAkoBo4OXkiZg7jxoWi+60wZASmTHGpO1q0CI3lpLudkXbefTeUo8yi+9NKQkpGVf8IHANsAB4SkSdFpBT4H85d+T2gp6q+HONtEkJEjhWRhSKyRESujbC/UEQmBfZ/KCKdw/ZdFxhfKCLHJEumcMKj+//+99D4yTmpZuOgbVu46CLXnjXLVdA0jFQTPoux6P60krDhX1WnAj2At4DTgA+BfYHbgMNUNYbpOzFEJB+YAAzEzaCGi0jXOoedDXynqr8A7sHF9BA4blhAtmOB+wLvl1SirY3bmnkMrrwSmjd37ZtvttmMkVpefDH0MHPeeTaLSTMN9S7bCKzBpZcRYD0wQ1VrkiVYgD7AElUtD8TpPAucVOeYk3CZCAAmAwNERALjz6rqFlVdBiwJvJ/hNW3bhqpnzpwZWsYwdsCyACSB4Mw5L89mMR6QsJIRkR444/5w4HXcMllT4L8icruIJNMteldgRVh/ZWAs4jGqWoVTeLvEeS4AIjJKRMpEpGzNmjVJEt2IyVVXuQqE4KIODzssx93vIrN6dcg78aef4Gc/c+Pt2kFVlbey+Z7iYreOHawaWFMDu+5qsVlpJlEX5guB93G5yq5X1YGqOhHoDXwCXAu8KyK7J0m+SAla6q6tRDsmnnPdoOrEQGaD0nbt2iUootEg2rWDCy5w7Q8/tCwAcVBUBNdc49pLl+5YNdioQ3l57aUxi+73hERnHX8DvsHZXv4cHFTVxcBBwH3AwcBHSZJvJbBbWL8TsCraMYEg0FbAujjPNbwkvOytZQGIi3PPdSFH4LIA2GwmBsuXh2bHFt3vGYkqmZeA/VX1/bo7VHWrql4MDE6KZI7ZwF4i0kVEmuIM+XU9114GRgTaQ4C31WX9fBkYFvA+6wLshfOCSyrR1sxzOro/XpYtg33CQq4sC0C9FBe7GnAAixe7pJmWBSAKQY+yvDz4z38sut8jEkr/oqr1Ouaq6osiMqfhItV6ryoRuQhn+8kHHlHVz0XkFqAs4Cr9MPCkiCzBzWCGBc79XESeA+YDVcCFqlqdDLnCse9sIygpgT594IsvXN+eNOPCsgDEwQcfuJgscHExRx3lNiPtxFW0LJdIRqp/IwEGD3Zr5x9/7PqHHgozZngrk8+xUhJxMHAg/Pe/LuHb0qXQqZPXEvmSjh0jP5x06JD4A3RDi5ZF9MZK8MIljX0PI4uZMsXdDIJ2mCY5m1vVSBYffui+UwDnnGMKJgbpiPOrzyazRETuSVTZiOOkQFXNcxsunpETdOzo1svBxczYTMZoDEFbTEEBXLtDkhAjzdSnZO7ClVteLiL/EZEzA1Uvd0BEWohIfxH5My4+ZQqwOfBqGLG5+mrnowuhm4RhJMprrzkjP7hZzG67xT7eSDn1FS27EdgHeBQ4FHgI+EJEvheRL0TkfRH5SES+Ar7DVcEcA1QCp6vqwar6WWo/gpEVhM9m3n7bxc0YEYnmudiqVXrl8CXnn+9eRWwW4xPiNvyLSEtcrrIjgb5A+Fd9K/Ap8A7wQiZXwzTDv4dUVMAee7ga7DvvDAsWmKdZPWza5P5klZXwy1/C55+7kJCco7jYfW/qUlTkUiUYEUmmE0lDDf+XiEgfd0H9QVX/oapDVLUEKAR+BrRW1SJVPUBVx2SygjE8pqQkNJv5/vtQ24hKs2ahdFwLF8KkSd7K4xnl5aEoVXDKxWKu6iVa3HMy4/zqs8mMx2UwBkBEqkXkjwCquk1VV6vq+uSJY+Q0xcUwfnyo/9JLlgEgDkaPhvbtXfuWW6A66dFgGcBXX7mZMLip3NatFnNVD8uXw7Ztrn3CCamr4lufktmMm7EECWZdNozkU14Op51W2435yCPtabQemjULrQgtXOj+fDmXBeCmm9yrCLzyikX3x8Htt4fSEqWyyGJ9QQnLgGNE5F5VDXpOW7iXkRpKStzTZ/ij+Oef59CdsuHkdBaA998PxcWMHg3HHus2IyrLlsFjj7n2CSdA796pu1Z9M5kHgF7AKhEJ/vJvCiybxdosbZ/RMCornYfQ737n+hUV8M47nopk+JzgLKZpU1c2wohIeBXfPfYIzWLeey+11405k1HVe0XkG+A4nJH/COArYHlqxTJylimBsKrKSnjhBbcONHasUzSxXGGM3OS99+CNN1z73HMtuj8G0Wa1a9em9rr15vBQ1WdxFSkRkRrgUVW1wh9GaunQwVXPvPtulwHg7bdhwACvpTL8RtCYUFgI113nrSxGRBJN9X8zLhbGMFLPmDGh6pljx1r2R6M2L74Ib73l2qNGuaqXhu9ISMmo6s2qaomljPTQvj1cfLFrz5oVuqEYOxAtrqFly/TKkVYuupQiaNkAACAASURBVMi95udbdL+PSXQmYxjp5aqroEUL1z7llFAshFGL1atDMQ6bN4dME61bu5CRrKK42Nnnvv7a9aur3SzG4ql8iSkZw9+0bRuazWzY4Iy7RkwKC+EPf3DtL7+ERx/1Vp6ks3QptGsX6hcXW3R/HATzz9Yl1VV8fatkRKSNiLwpIosDr60jHNMzkKTzcxH5RER+G7bvMRFZJiLzAlvP9H4CIykUF8Mdd4T6r75qWQDi4MwzXdVhcKEjQdfVrAjQ/PxzWLPGtfPzraJqHHz2mfszAQwfnrro/kj4VskA1wJTVXUvYGqgX5dNwO9VdV9c+pvxIrJz2P4xqtozsM1LvchG0omUBaBfP3tqrYemTaGmJvK+jA7QVA1N05o0gTfftOj+OLjpJveny8tLbXR/JPxchvAk4PBA+3GcV9s14Qeo6qKw9qpATE874Pv0iGiknGAWgPA75mefhZJ1GbnFq6/C//7n2tdcA0cc4TYjKvPmuZAzcDHOv/xleq/v55lMB1WtAAi8xryrBLJFNwWWhg3fHlhGu0dECqOcioiMEpEyESlbE5yGG/6hstI9rQZtM+vWwfPPeyuTkX5qauCPf3TtVq3gyiu9lcfnBCP8998/NPbEE+lfVYy7nkxKLi7yFhDpI98APK6qO4cd+52q7mCXCewrwc10RgRLDQTGVuMUz0RgaTxBpFZPxsds3OjyYaxZA3vv7dbmm/h5Mu4tyawV4gsmT4ahQ1371ltDy2ZGRNL9/29QPZlUo6pHqmq3CNtLQGVAUQQVxjeR3iNQTO1V4A/htWxUtUIdW3CVPfuk/hMZKaVFi1BU96JF8M9/eiuPkT5WroTf/961d9kFLr3UW3mMuPHzctnLwIhAewTwUt0DRKQp8C/gCVV9vs6+oIISYBBgZaCzgfPPD0V233xzFgaBJI9orqkZac4aOTJUz+Caa2CnnTwVx4gfPyuZccBRIrIYOCrQR0RKReShwDGnAv2AkRFclZ8SkU9xZaHbArelV3wjJRQVhZZJli+Hffc1z6IohAdovvJKaPyss7yTKWGCgZdTp4bGrr7aXNjrwU/LoZ7aZPyI2WQygK1bYZ99Qm7M554LEyd6K5PPUYVDDnFJi4uLnWd4RoSVVFTAoEEhj7KCAjj1VJc4NSM+gDe88oqrExONnLHJGEaDaNWqdpzMgw9agGY9iLhKiOBWnf70J2/liZudd3Yu60Gqqy3wsh5qauCGG6LvT3WEf11MyRiZR3k5DBtW231m6FAL0KyHww93QZoAf/tbhmQB+NvfYNMm1775Zgu8jINnn4VPPnHt22+vHd2fjgj/upj/p5F5lJS4J9xwli718Z3SP0Tzk/BlFoDvvgulFOra1T2e5+d7K5PP2bYNbrzRtdu3h0su8VYesJmMkakEAzS7dXP9efMsQ3O2cddd8H0gecftt5uCiUEw8LJpU/e8BfDNN/CLX3grF5iSMTKVKVPgvvvg3ntdv6YGbjMHwqyhogLGj3ftAw+Ek07yVh6fE20m6ocZqikZI7M54gg45hjXnjgRlizxVh4jOdx6ayguZty42OHrhq8xJWNkPsF1+6qqUG4rI3N57z24/37XPuYY57FgZCymZIzMZ//9XZEMcK41vXubB1IUormv1vWj8JQRI0LtjPG1NqJhSsbIDm69NZQsc+5cuKXeXKg5SXgWgHXrXHlmcIbjqipvZdse3R++5Nm7t8U/1YPfPfdNyRjZQbdute+S999vAZr10Lo1XH+9a3/xBTz2mKfiOLeo8MRqRUVWVjkOYiWjTnfgZSRMyRjZQXk5DB4c6ou4ipp2g4rJRReFyjSfe67HAZpz5zq/W3Duylu3WnR/PcyZA08/7dqnnup94GUkLBjTyA5KStxTsEjoF1ZRYTeoeigq8kmZ5qoqGDPGtZs2hTfegOees9inGKiG/mQFBf41X5mSMbKHyko45xx46SX3RPz++/Djj9C8udeSGfXx0ENuzQ5c4OVhh7nN2IGOHXd8ANi2Dfr29cfMpS62XGZkD1OmuFiZO+90/c2bXbZew9/88AOMHevanTu7NTwjKn4OvIyEKRkj+zjjjFBh8zvvhFWrvJXHiM2dd4ZsMePGuTU8I2vwrZIRkTYi8qaILA68to5yXHVYwbKXw8a7iMiHgfMnBapoGrlAXh783/+59qZNVgvez5SVhYwJBx7orNdGVuFbJQNcC0xV1b2AqYF+JH5S1Z6B7cSw8T8D9wTO/w44O7XiGr7iiCPgxMDX4dFHLUAzBp4GaJ5+eqiC1t13W/qYLMTPSuYk4PFA+3FgULwniogA/YHJDTnfyBLuvLN2gObNN3srj08JD9Bcs8bVhAPnrBetNECjCQZeLloUGjv0UItrqofFi72WIHH8rGQ6qGoFQOC1fZTjikSkTEQ+EJGgItkF+F5Vg9F5K4FdUyuu4Tt69qwdoPmPf1iAZj20bRuywS9aBIWFKYqdWbIE2rQJ9S3wMi6uuir6Pj8EXkbCUyUjIm+JyGcRtkTyeu8eqCt9GjBeRPYEIs25o1a1FpFRAUVVtmbNmgQ/heFbysvhlFNCfRFXUdNuZDG58MLo+5LmwfTGGy6vDbjZpgVe1subb8LLAavzmWf6M/AyEp7GyajqkdH2iUiliJSoaoWIlADfRHmPVYHXchF5B9gfeAHYWUSaBGYznYCoLkaqOhGYCFBaWhpVGRkZRkkJtGtXO0DTKmjWS9NUu8isXw/XBkyszZu7u+c//2mBlzGoqoLLLnPtFi38G3gZCT8vl70MBNOxjgBeqnuAiLQWkcJAuy3QF5ivqgpMA4bEOt/IASor4bzzYO+9XX/OHFi+3FORcp6bbw65LD/4IBx8MEyY4OKcjFoEK14WFMD8+W5s40a3EpwpiKo/H9xFZBfgOWB34CtgqKquE5FSYLSqniMivwYeAGpwCnO8qj4cOH8P4FmgDfAR8DtV3VLfdUtLS7WsrCwln8nwkJkznWEZXI6zF17wVh6fE8vJq1G3jPnzoUcP92h+6KEwfbp5lMUgZf+HFCAicwKmi9rjflUyXmFKJos54wy3LAPw+utw9NHeyuNjUnJzW7UK9t0Xvv/exTLNmZNZj+QekA1Kxs/LZYaRXO680y1oA1xwgXuS9qu11GOieSq1j+bjGQ9nneUUDLglTFMwOYEpGSN3KCmBm25y7aVLYdYsK24WhfDYmUmTQuMnn9yANwvGxLz+emjs/vvNlbwetm3zWoLkYErGyC3CU8yoWnGzOBg6FI4M+IFOnAizZyf4BuXlsM8+oX5BgcXExMG993otQXIwJWPkFuXl0L9/qJ+fb8XN6kEE/v5311aFPn0SDNBcvTqUxl8EqqstJqYevvoqFBQbCb8GXkbC6skYuUVJiXNnfvtt16+uhm+/tRtePfzyl9H3xQzQrK6GUaNcW8QVIps2zWJiIhCpTgy4MtnBuNVMxJSMkXtUVrqQ6SlTXGDg9OnOIJ2WjJA5xt//7jItgwvAHDLEbcYORFPW332XXjmSjS2XGbnHlCnwyCOhRe8tW+Dqq72VKRtZsSJkA9tzT/jjH72Vx/AEUzJG7nLGGXDUUa794INuRmMkh1WrnIvyxo2u/49/mHNFjmJKxshdRGrf/M46y2JnksWZZ4YMCWecEXJPM3IOUzJGbrPHHnDrra5dXm6xMzGI5tFUWBjWCcbEvPFGaOzJJ20WUw/BCV82YkrGMCx2Ji7CAzRVQ8ply5aQO3PJ5qWszNstdFLTphYTEwfXRqv7S2a5K0fClIxhlJfDsceG+iLw29/ajbEetkRIN9ufaXSqWeE6eXkuEabFxMRk2jSXhBrcam11dWbUiYkXc2E2jJIS6Ny5dt2Z+fPtxpggu7KSCQQqnhUVwWuvweTJFhMTgWgxMQsWON2cTWTZxzGMBhKsO7Pffq7/6aeumJYRF0INj3ImO7PeDTz7LBxxhNWJiUK0mJhvv02vHOnAZjKGAaEb4dKlrt7Jjz/CyJFO2YTXojd2oCMVzKAfe7EEgEc4k7NOSqSCupHN2EzGMMLZc08YP961V62CESPgsMMyf2E8hdzNlfwioGC+ZHcuYzxVVR4LZfgG3xYtE5E2wCSgM7AcOFVVv6tzzBHAPWFD+wDDVPVFEXkMOAyC83dGquq8+q4bqWjZtm3bWLlyJZs3b27YhzF8QVFREZ06daKgoCD2gaowaBC8/LLri8Do0XDffakXMoPYLMUUseNv4ieKuOOPP5kneBSqq6FJjDUkn96S6yXjKmOKyJ3AOlUdJyLXAq1V9ZoYx7cBlgCdVHVTQMm8oqqTE7luJCWzbNkydtppJ3bZZRfESsVmJKrK2rVr2bBhA126dKn/hKKiyO5TRUXw00/JFzATqahws7zFiwHQJk14uvq3XKl3U0ltp4kOHWwyGOTmm0NljSLh01tyvWRiZcyTgMcD7ceBQfUcPwT4j6puSrYgmzdvNgWT4YgIu+yyS/yz0WXL3A009Abm1lyXadO2KxhEkJoa1mvLHRQM1JOpOcvp2LF2aYRYCibTY2Ii4Wcl00FVKwACr/UVfh0GPFNn7HYR+URE7hGRwkgnAYjIKBEpE5GyNWvWRDsmAdENP5LQ/7CkBH71q1BfFebOzc67QENYsCCUwr+w0Lkrjx5NB2y6UpdYCjY8HiYbYmIi4amSEZG3ROSzCFtCrikiUgJ0B8Lqu3IdzkZzANAGiLrUpqoTVbVUVUvbtWvXgE9iZCWVlc4Wc8ABrr94sdllAJYsgd69nQeeCLz4ogtmnTCBIZi7slEbT5WMqh6pqt0ibC8BlQHlEVQi38R4q1OBf6nq9qrYqlqhji3Ao0CfVH6WIHWnxnFXD6yH/Px8evbsuX1bvnx5UuQNMn78eA466CCGDh3KwoULk/reGcuUKS7FzCuvwK67urHLL4d//zt3Pc5U4Te/Cdml/vCH2tkSDKMOfjb83wWsDTP8t1HViEU/ROQD4DpVnRY2VqKqFeLWSO4BNqtqjAxBjkiG/wULFvCr8KWTmHJH39eYP3WLFi3YmM1Z9NJEIv/LWrz/vlMs27ZB8+buJnveebk1sykuhkg2rTBniFjf/08+ge7dUySbj0nVPcFvZKLhfxxwlIgsBo4K9BGRUhF5KHiQiHQGdgPqFgN5SkQ+BT4F2gK3JUOoyy6Dww+PvsUi1nmXXZa4LI899hgXXXTR9v7xxx/PO++8AzildMMNN9CjRw8OOuggKgMLw5WVlZx88sn06NGDnj17UlZWxsaNGxkwYAC9evWie/fuvPTSS9vf8y9/+QvdunWjW7dujA/Gj4RRXV3NyJEj6datG927d+eee5xH+dKlSzn22GPp3bs3hx56KF8EaryPHDmSSy65hF//+tfsscceTJ7snP9UlTFjxmx/n0mTJiX+B0klBx8cuiP8+CPU1OReIs2JE2v3i4t3SH4Zy2S1337Jnd1nAu+957UEPkBVbQvbevfurXWZP3/+9vZhh9U11SVnO+ywHS5bi7y8PO3Ro4f26NFDBw0apKqqjz76qF544YXbjznuuON02rRpqqoK6Msvv6yqqmPGjNFbb71VVVVPPfVUvffee1VVddu2bbp+/frtr6qqa9as0T333FNramq0rKxMu3Xrphs3btQNGzZo165dde7cubXkKisr0yOPPHJ7/7vvvlNV1f79++uiRYtUVfWDDz7QI444QlVVR4wYoUOGDNHq6mr9/PPPdc8991RV1cmTJ+uRRx6pVVVVunr1at1tt9101apVsf8oDSD8f5kwX3+t2rlz6J+Wn6962mmqFRX1n5vpzJmj2rx56LM3baqal6d6/vn1nhrre59tdOgQ/2++QwevpU0uQJlGuKdaWpkE6dkz9v5YxRXDPWITfd/i4mLmzas3lnQ7TZs25fjjjwegd+/evBnIw/X222/z5JNPAtCkSRNatmzJtm3buP7665kxYwZ5eXl8/fXXVFZWMnPmTE4++WSaN28OwODBg3n33XfZf//9t19njz32oLy8nIsvvpjjjjuOo48+mo0bN/Lee+8xdOjQ7cdtCYs5GTRoEHl5eXTt2nX7DGvmzJkMHz6c/Px8OnTowGGHHcbs2bM58cQT4/7MKednP4MBA+Dhh12/ujo3Eml+9RUcd5ybwYGzwYwb52Y2lvyyFvV5kuUipmQSJMKKUS1irb8GVrKSRpMmTaipqdneD48BKSgo2O6ym5+fT1WMPB9PPfUUa9asYc6cORQUFNC5c2c2b96MxvGraN26NR9//DGvv/46EyZM4LnnnmP8+PHsvPPOUZViYViVq+A14rmWL1i3zlXQnDoVvvwS5s2Dv/4VLr3Ua8lSwxdfOE+yTYHws/HjQ581mJ/eMGLgZ5tMRhJtTToV4RWdO3dm3rx51NTUsGLFCv73v//Ve86AAQN44IEHAKiqquKHH35g/fr1tG/fnoKCAqZNm8aXX34JQL9+/XjxxRfZtGkTP/74I//617849NBDa73ft99+S01NDaeccgq33norc+fOpWXLlnTp0oXnn38ecArk448/jilXv379mDRpEtXV1axZs4YZM2bQp09aHAITY8oUN5N5553QDObyy91TfbZ5nG3d6mZuQQVz6aXZq0yNlGFKJsnUrR4Y3FJx7+nbty9dunShe/fuXHXVVfTq1avec/7617/y5ptvsuuuu9KrVy8WL17M6aefTllZGaWlpTz11FPss88+APTq1YuRI0fSp08fDjzwQM4555xaS2UAX3/9NYcffjg9e/Zk5MiR3HHHHYCbHT388MP06NGDfffdt5YzQSROPvlk9ttvP3r06EH//v2588476ejnZajOnV0AYosW7h98/vnw7rvZU7q5uNgFWa5aFRr761+T7uSQbFd/LwlbVDDC8K0Ls1c01oU5U3jvvfdYuHAhZ555pteipJWk/y8LC90Tf10yOcfZli1w4onwxhuhseJiGDwY7r47YU0QrUBXNDLllpStn6uhZKILs5EinnnmGX7/+99bqpxksHy5c28O5/DDMzfH2ebNTpmEK5jCQqd4GlhGOdLsPhtIRMHkcjYiUzI5yPDhw1myZAkjR470WpTMp6TEFTkLV9jTp8PMmaEsxZlipykvdx50r73m+m3awNlnw4cfuvQ6mfI5fEA6lsszBfMuM4zGUlnpbDJ77w1XXOEW53/7W6dgZs50dhq/ZwbYuBH69YPvAiWb+vVz6XR22sn10+hJphrbS9PILEzJGEZjCa9hv+++cNRRTtFMC2Q5uv9+t/nVThOpds6MGdC+vSfy5tVZX/FjLZpwfwgjNrZcZhjJ5MgjXVbi8NKH+fkwdKg/7TT//a+zuYQTIV1MsknERuGHWjR1E98G86Ua9WNKxjCSzUknufLNQaqrnSF9/nz/2GhWrYIuXWDgQPjhh9B4cFbTQCN/vGSaM4AZ+RuOKZlUkWSjr6X6zzCqq11mgF//2vXXr4ejj/ZHLM3atXDIIc4zDqBZM1cz54IL4IMPfGnk93M8jRn5Y2NxMnVIWpzMBRfAAw8kLR28pfpPDmmPeaqpcctRkdL6pNtGU1Pjrrlt2477fGAvStTYn6pbl8W/NAyLk0kW9eX6z893v5b776+dDj4/P+m5/v2Q6h/gv//9L7169aJHjx4MGDAAgHXr1jFo0CD2228/DjroID755BMApk+fvn02tv/++7NhwwYqKiro168fPXv2pFu3brz77rsAvPHGGxx88MH06tWLoUOHbleynTt3ZuzYsdvlDZYRiHZNT8nLcwkm+/evPZ6fDxdeCEuXpn4JraICSkvhwAN3VDBpsL9kGn6wAWUVkVIz5/JWX6p/r3L9+zXV/zfffKOdOnXS8vJyVVVdu3atqqpedNFFetNNN6mq6tSpU7VHjx6qqnr88cfrzJkzVVV1w4YNum3bNr377rv1tttuU1XVqqoq/eGHH3TNmjV66KGH6saNG1VVddy4cXrzzTerqurPf/7z7Z9hwoQJevbZZ8e8ZtT/ZToZPVpVxKXHD/+/t2jhxkePTs11ly1T7d699jWbNXPXLCqKO11/OkgkTX4yU+c39rqGA0v1nyTqy8kPsGiRe3rMy3OzmZISF0PRiPf1a6r/Dz74gH79+tGlSxcA2rRpA7jU/S+88AIA/fv3Z+3ataxfv56+fftyxRVXcPrppzN48GA6derEAQccwFlnncW2bdsYNGgQPXv2ZPr06cyfP5++ffsCsHXrVg4Oi6wfPHjw9s82JeBCHO2arVq1ivvvljKCsTSjRsHYsRCcLQaXQP/xD7cVFrpZxbBhMGlS4saHigoXozN6NJxxRuSEWps3h2TxUbr+SJO5RJbQKit3PD4e9+fGzFzMyF8/vlUyIjIUuAn4FdBHVcuiHHcs8FcgH3hIVYMVNLsAzwJtgLnAGaoaIclUgtSX6x9cWo6Skto/4vBYiiThh1T/qhoxPU2kc0WEa6+9luOOO47XXnuNgw46iLfeeot+/foxY8YMXn31Vc444wzGjBlD69atOeqoo3jmmWciXjdYLiD8s0W7pi8I//+/+CKsXAmnnAJ1M2dv2eKWtVauhDFj4Ikn3J2zomJHxRM+1qwZzJ4N113nXgNLjrUoKnLXDM8/luXp+iMpnsYQx0/CqIOfbTKfAYOBGdEOEJF8YAIwEOgKDBeRroHdfwbuUdW9gO+As1MrbhhTprgfb48e7jUFCgb8ker/4IMPZvr06SwLrOmvW7du+7lPPfUUAO+88w5t27alZcuWLF26lO7du3PNNddQWlrKF198wZdffkn79u0599xzOfvss5k7dy4HHXQQs2bNYsmSJQBs2rSJRYsWxfxs0a7pSzp1gl693Gy3SZ1nvRUr3N3sn/90+/PzQ55pQ4Y4JTFunHOVnjHDBYC2auVidGbP3vFaIk7BbN2actfkZGMzhczHtzMZVV0A9T6J9gGWqGp54NhngZNEZAHQHzgtcNzjuFnR/amS1wvCU/1369Yt7lT/5557LuPGjWOXXXbh0Ucf5fTTT+eEE06gtLSUnj17Rkz1D0RM9d+uXTsmTpzI4MGDqampoX379rz55pvcdNNNnHnmmey33340a9aMxx9/HHCu0tOmTSM/P5+uXbsycOBAnn32We666y4KCgpo0aIFTzzxBO3ateOxxx5j+PDh26tq3nbbbewdY9kx2jV9S2WlW9YKznjnzYNvv3XLreHU1MBnn7n2rFluCyeg2GtRUOBidTZudPEwPlsai5fGLqElE1N4DSSSocZPG/AOUBpl3xDcElmwfwbwd6AtTvkEx3cDPotxjVFAGVC2++6772DQ8sxYnEJmzZqljzzyiNdipB3f/y9Hj3bG+KZNnXH+F79QbdMmtuU5L091331VjzjCtX1m0E82qfC7MaN+4yGK4d/T5TIReUtEPouwnRTvW0QY0xjjEVHViapaqqql7dq1i/PSmYul+vcxwdnN//7njPPdu8Opp7pls8JC93r22XDOOa5dVOTO69cPdt7ZnevTgMpkka4Zhc1ckoOny2WqemQj32IlbpYSpBOwCvgW2FlEmqhqVdi4gUv1P3z4cK/FMCIRbr8LGuUHD669rBZc8qo7FuncLCSS7kw0gDISZtRPDb61ycTJbGCvgCfZ18Aw4DRVVRGZhltOexYYAcSu/1sPGsWLysgcNFPvIvUpjyxWKPHSWMVjs5bU4VvvMhE5WURWAgcDr4rI64Hxn4nIawCBWcpFwOvAAuA5Vf088BbXAFeIyBJgF+DhhspSVFTE2rVrM/cmZaCqrF27lqLg8pKR9URKwhlty9KVRV9gucvqECl32bZt21i5cmWtOBQj8ygqKqJTp04UFBR4LYphZB3Rcpdl+nJZWigoKNge0W4YhmHEj2+XywzDMIzMx5SMYRiGkTJMyRiGYRgpwwz/dRCRNcCXDTy9LS5GJ5Oxz+AP7DP4g2z4DJCez/FzVd0hmt2UTBIRkbJI3hWZhH0Gf2CfwR9kw2cAbz+HLZcZhmEYKcOUjGEYhpEyTMkkl4leC5AE7DP4A/sM/iAbPgN4+DnMJmMYhmGkDJvJGIZhGCnDlIxhGIaRMkzJJAkROVZEForIEhG51mt5EkVEHhGRb0TkM69laSgispuITBORBSLyuYhc6rVMiSIiRSLyPxH5OPAZbvZapoYiIvki8pGIvOK1LA1BRJaLyKciMk9Eyuo/w3+IyM4iMllEvgj8Lg5Ouwxmk2k8IpIPLAKOwhVSmw0MV9X5ngqWACLSD9gIPKGq3byWpyGISAlQoqpzRWQnYA4wKMP+DwI0V9WNIlIAzAQuVdUPPBYtYUTkCqAUaKmqx3stT6KIyHJc6feMDcYUkceBd1X1IRFpCjRT1e/TKYPNZJJDH2CJqpar6lZcobR4S0j7AlWdAazzWo7GoKoVqjo30N6AqzG0q7dSJUagXPrGQLcgsGXck6CIdAKOAx7yWpZcRURaAv0I1NJS1a3pVjBgSiZZ7AqsCOuvJMNubtmGiHQG9gc+9FaSxAksM80DvgHeVNWM+wzAeOBqoMZrQRqBAm+IyBwRGeW1MA1gD2AN8Ghg2fIhEWmebiFMySSHSHWZM+7pM1sQkRbAC8BlqvqD1/IkiqpWq2pPoBPQR0QyavlSRI4HvlHVOV7L0kj6qmovYCBwYWBJOZNoAvQC7lfV/YEfgbTbi03JJIeVwG5h/U7AKo9kyWkCdowXgKdUdYrX8jSGwNLGO8CxHouSKH2BEwM2jWeB/iLyT29FShxVXRV4/Qb4F25ZPJNYCawMmwlPximdtGJKJjnMBvYSkS4B49ow4GWPZco5Akbzh4EFqvoXr+VpCCLSTkR2DrSLgSOBL7yVKjFU9TpV7aSqnXG/hbdV9Xcei5UQItI84DxCYInpaCCjPC9VdTWwQkR+GRgaAKTdCcbKLycBVa0SkYuA14F84BFV/dxjsRJCRJ4BDgfaishKYKyqPuytVAnTFzgD+DRg0wC4XlVf81CmX5kYwwAAAfFJREFURCkBHg94LOYBz6lqRroAZzgdgH+55xaaAE+r6n+9FalBXAw8FXj4LQfOTLcA5sJsGIZhpAxbLjMMwzBShikZwzAMI2WYkjEMwzBShikZwzAMI2WYkjEMwzBShikZwzAMI2WYkjEMwzBShikZwzAMI2WYkjEMwzBShikZw/ApIvKiiKiIXBxh362BfVavxfA1llbGMHyKiLQBPsLl0TpYVT8KjA8A3sAlzjxAVTd5J6VhxMaUjGH4GBH5NTAdWIZL094M+BhohVMwGZWI1cg9bLnMMHyMqr4H/BHYC3gA+CfQEbjEFIyRCdhMxjB8TqBOzn+AYwJDz6jqaR6KZBhxYzMZw/A56p4E/xU2NN4rWQwjUWwmYxg+R0T2AuYC23C2mM+BPqq62VPBDCMObCZjGD5GRAqBSUBzXCnjO4Du2GzGyBBMyRiGv7kb2B+4U1XfAMYCs4DzRORUTyUzjDiw5TLD8CkiMghni/kQOERVqwLjuwHzcLXn91fVcu+kNIzYmJIxDB8iIrvjFEkeTpEsq7P/JOBFYDZOAW1Nv5SGUT+mZAzDMIyUYTYZwzAMI2WYkjEMwzBShikZwzAMI2WYkjEMwzBShikZwzAMI2WYkjEMwzBShikZwzAMI2WYkjEMwzBShikZwzAMI2X8PyIGqUNqsSsUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0,2*np.pi, 0.1)\n",
    "y = np.sin(x)\n",
    "z = np.cos(x)\n",
    "plt.plot(x, y, marker='s', linestyle='-', color=\"blue\", linewidth=2.5, label='Função seno')\n",
    "plt.plot(x, z, marker='*', linestyle='-', color=\"red\", linewidth=2.5, label='Função cosseno')\n",
    "plt.xlabel(\"x\", fontsize=20)\n",
    "plt.ylabel(\"f(x)\", fontsize=20)\n",
    "plt.legend()\n",
    "plt.show(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos graficar os dados usando a função scatterplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KsvvdPKodXH2ygfvHjuLBoXXtx4IMeXOW0bd4EcYfuKHjGPfsPmx1Hl5UWVMqTk42jLt01fNPvHoi0NXpvk/ahGSPBLgRvi1zL+MN9Jc0U9PiLoxSfQdEnR1h/VQrDpYtNg8rsqVacVBxyqEH/niN6/ZnJ6xScW0/4fFX3sH9YxddcLoqd3+SwNBgDdVKcHU4oRW4d/+/WnHawea7rltpNQTK5fJqxbhLD1tNvtB393ki9zsLIvpTADcB+BEzX5XEZ8g2t8X9Y0eNK13bClp/kVt/n4NtN681BjP938G9hhVxxSnjpvWXYc+heLLXnDJZGaggGK3K7+23rA1dbGfD46+8gydePYE7P7FCm1bqPu4WKh58+zTOXphWynyuOYtHtm4IdKE1mjOhgvgqd6G7S1fJrarOX+i7+zyR+3YfRPT3AXwA4L/ZGAtp9xGNsfG6MqOmTIRZZmsj6nfBuLgtIsIYYVOrikfnWojH0QHWNWhJZBbFzaaPXIrvvvO+lVGzcSdVKw6WLVmEk5MNVPscfHBu2nqUa80XpNa16FA97w6T8j7uyq0qRhTixdTuI/fGAgCIaBWA58VYJIdOKevmOgCd2S1nz08rV9a6XlNebILa7rHimi1BaK1kpy5MGzOIFpcJF0L2dIqTMhEevmN9+7oHBcfTwJ3F4aXbbKj6ZKPDwElPqORZEMaCiO4GcDcArFy58pq33347PeF6BJ3C7e9zcK45G7haBMJn27hK2XYCWxA2jQejYuN2Uc2qTpO3PAOO4hzGFAb/oKU4iNLUUugek7HoiQA3Mz/GzBuZeePAQHxzjBcSKt8wAWAOHjs6uu94pPGi/hnYfmyO+Ysrl7cNTdjZ1U5ZX5Js430JG3hNourcG/DOyrefxOdmlcq+0Ls4mOgJYyG0cG/2VSN78ZH7volVIW56VeuJu65bifcVLqWTk42ufsCqNhE2x3zpjdNtZfn8kffCfTBfzACKQn+fE1pJJuEi2uXpaZVF5p4b1I5byaqubZIGUdXSRgzGRcRY9Ajemx24qJxsb/qgPj2PbN2AB4fWKX+kJaKuXR/1yUaHkrFVCo+/8g7GxuuhM4/cwK3JffKF61YGPn7j1ZcZ+xalgZt95abHJoWbUgtc3CG5fZwAxK5ks0hlN01nFAoQsyCiJwD8QwAfAvA3ALYx85+oXr9QA9wmv303/l6bGIIJ3eAilyhxENOcCx2Pbt2gnGFBcxHWvsXljipwr5xuQNbm/JLClSeurDAvpvvGNr4QduJd2hPyVDGfJGIyeaXww4+Y+c6sZSgCUWdI+NH9SG0yb5YtLuPC9Oy8tEtVaqQfdyXnKhkb5ddozoCoFVcJy+i+49h+y9rANF/3eEHtQrxyHnz7NHa98k5mhsIrT5ghR7Z0O5sE6KzfsWnUmXbFtnRxMCNuqB7BZoaECZ3fdmiwhpdGrsebO2/ErEYxzjKw9doVHW2nHxxah4duXaesJHbxDkB6aeR6q7gCszlorfqsocEatl67InQA+uRcC47HLbuyJo173ZY68f6ko95X7uNj4/XAzrV5c/FIFwczYix6BJ0P3famt/Xb6hSI27rcNSwvjVzfXiEODdawbIl+M+s/drVPb1yAlkEa/fz6toHq73PQZ6E0iVotxx+PsDPoW1yeF2DOmmqfg+GnjnTVYdYPwRw4NynZHc9NKI1pnlp5mGZrCAVwQwl2eF1FXh96mOpX25RFk7vDDVoH+ZptmhOG6TLrvsfvtvjY733L+F7LAuVApi7M5GJHAbSuwbnmjHXFtS0M8zwXXaucsfG61njlzcUjzQr1iLHoIbq92W39tqahNMD8Wgrve6p9TqACcZsTAubgtre691xzBvfsPtz22Q8N1nD/2FFjS/FuyYuhcK9blK6yJnTT+byo7judm8lm1yLkCzEWQhvTTGQvqqE0frxuLFXmkbdnVNAQJD8c8N9ew/TEqye07+8lFi8iY7PFKPi/9yjZSbpd5F3XrZRVfMEQYyG0Cdt9131cZQRc6pMNbXO+ZYsXtY/VbZGfbrfTi8S5g+rvc+YNTvJP8AsaTASo7xfVTrVacebN5hCKgRiLHifMijBqA7jRfceNhXE69f1+o9k+drdqfiEZijh51Nei3Mv2ZycCEx+2PzuB89OzgUZkaLAWuFN1Sq2BUqtG9gK42CVAjEf+EWPRw4RZEW5eM6Ada6k7VrdZLRWnlNk0vCJg09iwG1RJw/6ZJH5U0/+80+2Ai/fZ8oqDn5xrojl98WQYrUp8AGIwck7uK7jDslAruINQVddWK868FSGgn3lQ07TtdoOgWXdg7WW+cN1K7H7tROzZTl4651k0EdXDpap61nUZKBPhjYd+NdoHdkna1eJ5ZUF0nRWCUa34JxvNjlW8Tg3VJxvKFMiTk41c9EkqClG6zx44dgpbr11hLGjshslGs12MeWbKzlD0K2pgLq9WApsL6nagWbkPpYGgPWIsepg08tgvr1Y6Cpp6kXLJfGamQsBatRJJKdYnG9hzqI7tt6y1TmdVEef3s+3mtYEFeZvXDAQq4OUaY2cyokm1D5cGgvaIsehhVNW1qhVhWEXiTa/0tgPpVqHFSZ9T0sqzbHEZjoUhsPmhTDVnlZ9FCD93w4u3/1M3uzgGrM7XBAG4d/dhLHVKqFaceVXPB46dClTARGqjO8OsNAJJrv6zmp1RRMRY9DCqFgaqFeFd163UKtZqxbFqh6AyUqqW334ILSUeB43mLF4auR6Pbt3Q0T/KKRO+/rl1Vn2lmrMMGw/S8JbVgcf75Ecuxa5Xze1BdB/h9rJyv1Pg4oq8WnGUiwA/lyxdNO97tH2fF577d2aqifPTs3hk64Z2axel+3OqiYdvX6/8blVGIMnVfxazM4qKNhuKiC5h5g/SEkaIH11Vt26mdlBx3vZb1loF/lT1GkCrYM7kimEATrmEigPrILwKIrRbj2z9+AocOHZqnkwH3z6NKctIro0HSVVJ/dIbp+0+A+rzdBVYUEHkZKNpvWM4M9VE3+KLP/0br77M2BFYhzcDClBX6bsuS/d1QQFv/7GAZFf/YQpRFzqm1NkjRPQFZv7LVKQRUkNnRMIW57noskpcA2Trs59sNFGtOFjqlHBmqhnJUAAXU05dv793N+R2RM0bQefpVWBj43Xc++ThDuMVJlvKVdL1yQZ2v3YCW6+9aEirfQ7ONWdCFfy5intsvK6crOh3w9kagSTbh0e91xci2tRZInK/9YcA7GDm3CfCS+psNqh2I65yNg1nUlFxyliyqBR6Gp4K71CeqDKlhWsgvc0gx8brGH76CJoz8WYPVSsODm+7IfC5DTv2G69/aW6miG7WSX+fg77Fi9pKWZeO7R2c5J+HAcy/t4R46DZ19pMAfgjgKwBeJqKfj1M4IR7yMGhe5Vfe/uwEgOgug0ZzJjZD4coxNl7Hhh37YzEUSQbzXUPhbfM+uu94ZEOhS73VXWObWM0st+TV7RzPTDXnBak/ODfdEd8J6km151B9nqEgALddIx1i00ZrLJj5OwA2AHgMwMcBjBPR3WkIJtgRJVNkbLyOwa/ux6qRvVg1src9x7kbdDUdH/u9b8Wbs6nA5iPcuQ9xGaCkfdv1yca8BUA3fvqo5zwZ44wML81ZxrLFi7RJE0GLEEar9kRIF2M2FDM3mPlfALgJwFkA/4GI/oyIVhPRyqB/iUsttAmbKeK6Mbzb/8lGE8NPHenKYOhy6BvN2UhjT8PglAl9hgwqQstVElcldH+fg6HBGioxT6fz410AJJWlo8pQGhuvoxShkNAW14B5s6m8SGprfrC+y5n5mwDWAvifaBmOHwB4M+DfD+MXU1AR9sekcmM0Z7mrVMQ49El/nxO5hmDrx1dgKmBethcGlMHXsFScMrbdvBYA8NCtV8dyTB1uR12V66zb0okL07Mdi4WwSQlR0e2GJbU1P4RdEl09948A/A2AdwL+LZxhAjnA5sfkjWno/PRduTi6dFU4ZcK2m9fioVvXRWqJsedQ3TiCtVatWI1pNeF3lwwN1iLVKoRFp7S71efNWcaXnzwyL+4VtGtNCtVuWGZj5wcrY0FEDhH9AYAXAAwA+B0ANWa+MuhfnAIS0WeI6DgR/TURjcR57F7A9GPyxzR0RF2txeGqcGdaDA3W8PAd60PvMBrNGTBDWWDnlAmb1wzgg3PTXcnpDzi7BBU6pkkca/8Z5nlxrzgSAMLcFUGLlaHBGm67ptZeQJSJJLidEcaus0S0FsAutHYUPwBwFzMfSUE2EFEZwF8B+EcA3gXwHQB3MvMPVO/p9dTZoFoGQJ0nbpse6pQIo7ev1x5LJU8c7cX9nUrHxuuhBxkRWrGToECu21U1LgWom/eR53TcMJQ1abBB+GthKk4Zt11Tw4Fjp6yuSZkIs8wdc7x1KdlCfJhSZ011Fl9Cq8ZiCYA/AvDbzHw+dinVn/9LALYz85a5v+8DAGZ+SPWeXjYWUX44V47stVp19vc5gZW8puPHWatQJsKdn1jRnmtw13/6S+vKZ6C16j+p2EG5K9w4ve9OiXDJ0kXzpssBwPBTRxJtJ54mFaccaiHgfgdBRZlhrot736mMr78WQ+gek7EwVXA/AuA9AL/BzPtjlcyOGubHQN4F8An/i+bSee8GgJUrezcZS5f5pFLmqurX/j4H55oXZ1qcmWp2FD7ZHD/OrJQZ5vYgnI0fvhQvhzAUrutNpVxcF1ucq/7mLLezyuqTDWWrjzxQwsV+Tra4xYCmsbne16sU+NBgDb/7zPesjYV730k2VH4wxSy+AWBdRoYCCHZ5dtxtzPwYM29k5o0DA9E7e6ZF1CK6KD8cVUyDGYH562GOn1Ra5ROvnrAasVomaufn33ZNrW0o/BIRWsp86kJ38Yois9QphTIUTonaO6W/s4jzlMhcc2Lbg8vF3aEEIdlQ6WMqyruNme2Xd/HzLoAVnr+vAHAyI1lioZt2y1F+OKrOs2FSSIOOHyat0lXebpCyVq3g0a0blK+fYbbaAcwwY3nFQX2ygcdfeaf9Hr9E7t+qAU4LgbCKGgQcfPu09Xe8dFEJo/uOx9pFwHVlSTZUPsj1WFUiWoRWgPtTAOpoBbh/jZknVO/Je8xC5eO38cHGGexTyREUpAw6vm2solpx5nWr9Qbo83vn5Zcli0o4Px1x3mlIwga4vQTdN4Nf3R/KYPc5JTSas1hecUCEebEhCW7HT7cxi0xh5mki+iKAfQDKAP5UZyiKQDc+2Dg7ZG5eM9COD3j55EcuxVs/bhiPr5NXly0UR+ZUHuhzSmjOcuwN/UykZSiA7kadBsW6tt28NlRcx90NTTaaqDhlPLJ1Q+CiQwxIOuTaWADtyvFvZi1HXHTbblnXWjwMqt46b/24YZVlojqPoB1StymlNPc/edkE9zkl/OBrv4Kx8Tq+8o2jOGuoHC8q3ewsgM4FxdBgLXISgNf4+BcdrivX/QwhGWRSXspk7YN1g+sqxW2bZWJ7Ht4YTRRq1Qre3HljbgwFACzxnHePZMh2UHHKuO7v9Qc+Z9sKK2gBFKU638W9N2VudjbkfmfRa2Q5bMXGDRRmhwOYz6OblhFOuZWRMzZejzz8KAnOTDWxaeeLOHt+uidcai7+ojiV8rWJlasWQN3sVNx7U9Jps0GMRQZ060qK6q81Ke6wOxyb8+jqB8ytjJwnXj2hNBR9Tkmb6bPpI5fitbfOxB5b6JUqbRdvQLpbt2FNc09WDRX27qS+D85Nz6vJ8N6bSU7OE9SIsYiZpANv3fhrTUHpbnruqM5b9cO2oTnLgUF4L79/69XayXFv/biBa1f1h6oEX4ica87gnt2HseO5iQ5F7ae/z8HkVDPQgOuy+sbG6zgbUOvilKhjvrvudyRzs7NBjEWMpBF4i1LF7aJT3LqBMiYDqDvv4S2rce/uw4m4kGrVinFyXH2y0XO7gCQIU4ty49WXKY24bkGi+q6mA1xTScyIF7pDjEWMdKPIbenGXxu0IjMdw8YA6s77pZHrcfDt08YdQhQ2rxnArgSOK+g5cOwUaiFdQWPjdfVChWG1qJJ02WyRbKgYSSPw1k37A7eaW5WREnQMm8wT03k/OLQOj27doJ0BHYUDx06JnzoDTk42QmX1uQsOHaZspm46HwjxIDuLGPKHU0EAABspSURBVEkq8OZdUVX7HDglUgb/TLgrMVufr8oQ1Ccb2LBjP4jUWUre8/a6FWyyssiirqI+2UB/wPUIi1OmnohruIFlYL6L5vTZ82iEbfeh4fJqJZQryDYjzv/b8d73pYCaj7h37YIeMRYxkkTgza9Yz0w14ZQJ1YqD9xvR2h+E+aHr4hymTqSb1wx0uA42rxnAgWOn0GjOaNNhbTMs3ethyopS0d/nYNvNreDq/WNH8cSrJxIfI5o0fn//4Ff3hzYWlblWG0HtX9z72Tarz3Zn7d3x+u971Xci6bLpIcYiRpIIvAWtypozjGVLFuHwthu6ktVGLlOcQ8fzR97D7tdOtFf9bsM/FxuVbGMEmjOMZYsX4dz0bKgiOa+hAFruMneWxqqRvfYH8tHtTicqqoSKaCNvqd3sMWjY1qadL1rf47YZcV6DYLsb8e/aJa6RHLluJBiFvDcSDItqeJF/slySZDkBrlpxcHjbDYnJoGqU+JH7vhl5h/Ho1g2hp/zFib+4bsdzE5E67qpat/gXD+7uQ1VfYdsTzPt5NkO7/N+dTNXrjq4m5RWRXjMW3XSpTUuWpHnU00AuKRn8/v6on9Hf52D8gRusJxTqiKNqvQSgm2jFW74Fien6u8oZQIf7Uefic8qE0c9fHOur+oyg0asm2WSqnh0mYyHZUDkn615SJllU2L7OBm+WTJAMcYxfcifd3bP7cFfGyG0FsjyGzK84lnHdGAoCOrKNTDGCRnMGO56b6Mhc2nOojjs/sUJ9X/DF+Rmq619xynj4jvV4c+eNeGnk+o7dgrQBSRYxFjnHP7yoWnGw1Cnh3t2HYxsy040s/X0OCK0VtZsaWyZqBbBjGqJXn2y0zzNomFPe9sb1yQbOXpiGU4p/imCaMNCRzmqT2XdmqhmYbn3g2Cll6nZzlvHEqyeUrip3aJcpNhLmcSEcEuAuAG4w2lQgF3dwT3U81TH98gV5HMolwk8tWWQ109mL9zz9MmTlHtPRnGHk2VZUKw7OT88a4wj+VXk3FfknJxsYGqzhXkWb8m5jPNIGJFlkZ1EgdAVycRctRTmeKoPFOyv74dvX4/C2Gzp84SZ0RVth3GNpkuf25Tetv6wdW9DBwLwd7NBgTWsoKk5ZWXzprvBVK31d+3Kb+081QliC2/EgxqJA6Hyycff4j3I8lXyzzIF+5lpI94Dq+K6SiMvt5cUpU+Y7hGWLy6FcWjavPHDsFIYGa1bfgV9Rq95TJsJDt67D9lvWauNsqjicNqYBu/t5aLCGl0auV8Y1hOiIsSgQOp9snME9XR8f3fHC+oxVSqO/T78yDWJosBb7gKRli8sY/fx6/PTSeNuUhMEpE77+uXW4ZKm9x5jRas2uU7zu92i7K/MqatX39vAd69suQt0KX/X8g0Pr2o+b5BbSR2IWBULnk1WlG4YN7pn6+OiOF9Zn7CoPbx3AkkUl3Hj1ZdhzqN6Ry795zUBbxqBCsbg5e6GlIMPGV1TYtDDxUibC1o+vwOi+46HrJF5+4zTuum6lMl3V/R6HBmvWjR7d+0tXfOr/brxzs72oYl/u44Nf3R94zlXFQmIhk1YhohiLAmGqEI8juKernDUdL2oF+zlPhfZko4k9h+r4xZXL8fIbp9v+cQaw51DLDeI1JPXJBoafPpLYGL04A+d+nd3f52iNwAyb53koPwstV9Odn1gReIxVP1NpJwaE8bKtGtnbrkkxFex106JfZVTPNWdCVY/3OmnOI89tUR4R3Q5gO4CPAbiWma0q7XqtKM+Ev8kgMyL3jAL0lbOPKlaJ3aDKZCoHNI7TPW7CdW1FqWROilq1gv/3/rlEK71Vk+ni/oztt6xV7m6jFMXZFjUu9ArtOAsRTUV5ed5ZfB/ArQD+OGtB8kpQk8GKU1Zu/W1Q9fGpeTqNdoN/y6xauasUaFTFemaqiVq1ghuvvgzPHHo3UtNBHar5DjqSTvclmJs9xsFko4nhp44o+2FFiTPY9pNa6J1n0yxEzK2xYObXAYCSSHHpEZIYttRtrroqnuCuOr0tLPx/2xB1Z+F+3p5DdZybjtdQRIUIuHx59LGzJtL0GegaJ0YpigvTwHIhB73TnEfeE9lQRHQ3ER0kooOnTgWPBu1FklhVdJOrHlSbMfzUEQw/faR9Q/tVCsO+XYdNeqWJRnMm9qwpINougbn7GpFatRK6ZsWGPqcUSwV61KK4oPswSpZcr5NmO6BMdxZE9G0APxfw1FeY+c9sj8PMjwF4DGjFLGISL/cktaqwbV/uJ7CdukVlmu4VqsZxu155J3dtPqLgXrMoO6YSoa0UorjBVDglwu/fenVbvqjHVXWhtcV/H94/drTje1/oFdppziPP1Fgw86ez/Pyik7f2BlF3NDpX1Awz+vuceT+AA8dO9YShAC7uSGaYI7nkXLqZO+KnOcv48pNH8PAd69tB0rDzPQiItdPr2Hgdew7V510fAnDbNdEWNr1E1MVdWHIbsxDMpLmqsME2KOnFRkGemWri3zzZ6ic0NFjrWR+165KzNRiuUgc674VujekM87wUzLBZVXG4hkxjVd304CjHy/q3EhdpnlNujQURfQ7AvwMwAGAvER1m5i0Zi5U70lpV2DC8ZXVHVkwJQLlMaM5cfMw7LMfWuMwysP3ZCQwN1oxGKY45EFnhvS425zHDjHt2H8b2Zyew/Za17dV8HM0VvVXbZy9MW7+PgK53t3GMVfWnlX9wbnre1Mak6hHSIs0aCyDHAW5m/gYzX8HMS5j5Z8VQFARfTLRcJly7qr/tMikT4a7rVuKtud49YfpDuStb3UyLWrWCu65bGRj0KwJufvxbO2/EI1s3tAO8uiZ7QOvaePs3hQmcO2V9A7/RfcfnGXsTDLOyGhuvY9POF3HlyN7AVvu2Y1WB1oxx/3H8yRZnppod8bNueqflgbj7wZnIrbEQikeQUmnOMF5+43R7ZTjDjD2H6pGUmktQpswjWze0DZC3x5A3o0uX3BO2qaGfilPuuuGgP97kbYr38B3rjdlJjeYM7pmbcwJAOTvCz7LFagdDmSi02093LcfG69iwY397yJSqo7HtZ7qGwH8cW2NTZJdm2sOecuuGEoqH6ib1r0m9tSBeX7vJbeJNnTS534KeV/VA+sJ1K/HgUKtdd5hArj9T6x7FnAYd/X0OJqfUFfdeV4ptyZGrNB+6dR1mLTKsdLGIGeZQ7kJdgkVQNpOLvz4oSvzLexxbhVnktNs0aywA2VkIMRLmJvX+mN0V9KNbNyh3GU6ZsO3mtV3J9+DQOnzhupXzXGJeQwFAmcsfhL/1epj3Ai0jNf7ADcp22n5XSpj5GO4uo9RlUaub/mqz+9PV5IyN143pzt57QlU/YIMb7DVR9LTbtEcuy85CCIUu+yJM+mbQj9m/y3BrD7rN1/fy4NC6ecbBT5hSB+85jI3X8cG5ziCwU251jn3+yHvtFXx/n4NtN681nk8Yv72KbvpOuYrHZvdn6kU0uu+4MVjvvZ6qTD+bHaj7Wv+96JQJyxYv6qp3Wp5IOxtSjEVOKEJanyn7wpV3+7MTWteGqW257ryTvE5j4/VQ6aHec9jx3ERgAeKyxYuMBkqFjSvFNvMrStGffwYFgMAeUE6ZjKtZ07kE3ROqe0G3IAkycHn+TXVLmtmQYixyQFwpcEkbHJteVEODNe0MiG52CUmmCprmeARx7+7DGN13HJvXDCi72b7fRSM/k9++RMAf3rEBgF6BAi2XWRiDEdQ4cnTfcaVBNF1/3bnY7rSAztX08ooDIgTGffKUVt4LiLHIAXE0BEwj59o2+0L1um6repNonKg7tgk3A2eXZuaEje9cZeRNbr1Znv/d6nZ0l1cr2LxmwGo+hlMiTF2YxpUje+fJo/pebQxi0LkQgLt8MSMbxAhkgwS4c0AcKXBp5Fzbjk0NO17Vlm6vky63v5t0Q91a3eSeCWq+6KZ/uinCOtzzGBqsabOlhres7gjwB0EEgILTUVXfX4moo07CjyrdOYp7TsgGMRY5IA7lmkbOtW32RVJZGt1cJ1VHXLegq9usoSCqFSdSENtr5N1WGyq8ylw32MmV48GhdXjjoV9VdvplRketjCvP5jUDge9zW4PYGAy3biQo+0vIN2IsckAcyjWqIjVV0nqxbV/eTZtzHd1cJ1VHXHcF3e20Or8SrThlbL/FnOqrMub1yUb7O7lp/WXagjybHaT/+w27y3NngaiuUtGroQUzuR2rGpWijlXtNjjtj1kA5pGTUd6TNWGvk/t62wIvVyW7Pv6933uvvWLXZR5t+sileOvHjcChT7rHzp6fNmZgVZwybrumhgPHTinPgwAst2j2536/QPDM9iWLSoHHsAmOE4A3E5itIaSDaayqGIseIqwijXN+b57wGogoTQVVw4R0c6GrFQeHt90wT4aOPP8SATTfzeOUCWDz3A/3O9F9Z0GNHHXHUk01DDIiNsH/ot83C50iz+AWQhI2SyTt3jK2dLPL8ivpOJdCuvTPyUazHWgeG6/jy08e6ViJBynx5kxrXkff4kXa1uLud6KbYWLbpvzkZMN4jcMWwxW9GlowI8ZiAZN2bxkbuk0Btk2BJQqu1ta17Bjeshr37j6sVMKuz/6+Z46GioGcmWpi/IHWrkS1c3C/E1OxmXfBoDrW8opjLK60KYbztpp3DcWmnS/2dBHcQkaMxQImD5P2/CvcqQvTXdVS2FY93/WJldj9nRMdmT9npprYtPNFbF4zgAPHTnUoPlUzQvezo9RrENDeldh8J7Y7SFVtQ1BMwnSNTUYq7dkKQvqIsVjAZN0SIUjBqAjTRdQUzGYAu187ga3XrmgHjb2xjfpkY55B8Cq+B4fWzQt6+z9bJ6dTokBXFAOBXXi7/U78PZ1M8RvTNdYZqSQLJoV8IMZigZNlNWyYVbita8y2mWFzlnHg2Clt0NiLV/Ftu3mtcvWvqqIuE2H09vXKNub+Lry6DDbd6j7ouaHBmtU5duN+zGv8S4gPMRZC6oRNZw3jGgszi9pVZLYKzX2davV/8O3TgYbCKbUMhds3SxWTMAWdda4eAFo3kOlad+t+zGP8S4gXMRZCqgSllPqpVhwsW7IoshvGJsgLXFRktoN2llfUw5fceQ1BXLL0YqM9VUxi85oBo8/fVO2tcwPp6iTiaAGfh/iXkCxiLIRUMbme3MrnuFxjw1tWY/jpIx2BbKd0sa22retK1xFEN69h0hffWOqU2p9VrTjYfstaK5+/rtpbhfseXXZWHLURWce/hOQRYyGkOktD5/KJc8iRi3usHc9NtIPSroL2z2owua78St+L7rzcHUzQWNHz07Pa93sfjzJq1P1s3WjUTTtfjOW6SzfY3ia3xoKIRgHcDOACgDcA/AYzT2YrVe+RdsqjSuElWf1ro8Tc16gK6gC9/111XoTWzkU1VtTdPahadXg/M8wkQpepC9MYG69r3ytproINeW4k+AKAq5j5agB/BeC+jOXpSdJobe4l7bnBYXANZ5ChMMkYdF7uvAY3sK3asdQnGzh7IWAka4k66ituu6am7BgbxJmpZtsQuM0dg5BGgIKJ3BoLZt7PzO4v6BUAV2QpT6+SdspjUh1p40AVTykTGWU0zWvQXc8yUUdMBZgfGHc5cOxU6BYm3tjHSyPXK42NpLkKOnLrhvLxmwB2q54korsB3A0AK1euTEumniCLlMesfNum2IxKWc4yW4/8VL1O56ZSBZ+DYiRRFbpN7MP/nRdhLryQHpnuLIjo20T0/YB/n/W85isApgHsUh2HmR9j5o3MvHFgYCAN0XuGPLuFbLCdxzE2Xsfw00fmDz96+si81yc14Q/Qu6lUrqGgz9XJopuA5499mL5z3QQ/YWGSqbFg5k8z81UB//4MAIjo1wHcBOAu7rVe6jkhz24hE2EU2o7nJjpcPc0Zxo7nJtp/J2k4dW6qMJ+reu2jWzdgVvMT8cc+TN952rEsIf/k1g1FRJ8B8DsA/gEzT2UtTy9TtJRHXQW4qh+RauSo9/EotQJhXDWq6xzmc1WvBVqzsINcWv19neNdTd+5tO8Q/OTWWAD4IwBLALxAre31K8z8W9mKJGSNTQV4NwotjOGMM+04zOcGVY/rsri23Wwe7+pH2ncIfnJrLJj557OWQcgfNs0H01JoJldNWsHhbrK4VEj7DsFPbo2FIARh2jWoFFpVUfRG1BqXGkWh69pvpFno2G0WVxDSvkPwI8ZCKBS6lhe6diHbb1kbOJ/a9dxEUegqWcpEqc52SMplVLRYlpAsuS3KE4QgdNlAL41crw0uj96+vp0BFJRmGjbbRyWLqm4iqeBw0dOfhWIgOwuhUHTjHvGulK8c2Rv4mpOTDesMJ5UsqkytElF7fGqcZOUykqK9hQX1WvnCxo0b+eDBg1mLIeQc1ZyL/j4H55qzHYHdMIFiXcZW2GPllaBz7JVzW6gQ0SFm3qh6XtxQKWJbbSwky9h4HWfPdzbuqzhlMKuHCNniFr3F4erKK1K0t/AQY5ES0j4hH7jfgz8zigi47Zoa3g/ImALCxxuGBmvKiupeKGyTor2FhxiLlJCVWD5Q1SQwA3sO1VHtcwLepc8sUu0Yk+w1lTW9fG5CMGIsUkJWYvlAd70bzRkwoyOzyCkRpi5MB7oPdTvGXs5S6uVzE4IRY5ESshLLB6br/X6jOa/JXrXiANTqIRXkPjTNzi5qk0YTvXxuQjCSDZUSkj2SD0y9pfzjXVVZU+7rrhzZqxxGRICklAqFwZQNJXUWKSHtE/KBe713PDfR0Yk2yI1ich/qKsq9OxHvZwtmpIYjf4ixSBFpn5AP3O/BRiGZWmkENdzzk2Srj27Iq0KOs5uvEB9iLIQFi43xNnVf9e8YVS6pvCUy5Fkhm+JAQjZIgFsQNNgEcocGa3hp5Hq8ufPGUCNSsyTPqdySOZhPZGchCAbCuA9t50Bk7QLKs0KWwUv5RHYWghAjNjuRPFTz5zmVW2o48onsLAQhZkw7kTz45PM8CU8yB/OJGAtBSJk8uIDyrpAlczB/iLEQhJTJi09eFLIQhtzGLIjoa0T0PSI6TET7iejyrGUShDgQn7xQRHJrLACMMvPVzLwBwPMAHshaIEGIA+mrJBSR3LqhmPknnj+XAcp6J0EoHOICEopGbo0FABDR1wH8UwDvA9icsTiCIAgLlkzdUET0bSL6fsC/zwIAM3+FmVcA2AXgi5rj3E1EB4no4KlTp9ISXxAEYcFQiBblRPRhAHuZ+SrTa/PaolwQBCHPFLZFORF9lJn/79yftwA4lqU8wsIl69YcgpAHcmssAOwkotUAZgG8DeC3MpZHWIDkuTurIKRJbo0FM9+WtQyCkIfWHIKQB/JcZyEImZOH1hyCkAfEWAiChjx3ZxWENBFjIQgapDWHILTIbcxCEPJA3ruzCkJaiLEQBAPSmkMQxA0lCIIgWCDGQhAEQTAixkIQBEEwIsZCEARBMCLGQhAEQTBSiK6zYSCiU2j1ksoDHwLwt1kLYUlRZC2KnIDImgRFkRMojqyunB9m5gHVi3rOWOQJIjqoa/mbJ4oia1HkBETWJCiKnEBxZLWVU9xQgiAIghExFoIgCIIRMRbJ8ljWAoSgKLIWRU5AZE2CosgJFEdWKzklZiEIgiAYkZ2FIAiCYESMhSAIgmBEjEXCENHXiOh7RHSYiPYT0eVZyxQEEY0S0bE5Wb9BRNWsZVJBRLcT0QQRzRJR7lITiegzRHSciP6aiEaylkcHEf0pEf2IiL6ftSw6iGgFER0gotfnvvsvZS1TEES0lIheI6Ijc3LuyFomE0RUJqJxInpe9zoxFskzysxXM/MGAM8DeCBrgRS8AOAqZr4awF8BuC9jeXR8H8CtAP4ia0H8EFEZwL8H8CsAfgHAnUT0C9lKpeW/APhM1kJYMA3gy8z8MQDXAfiXOb2u5wFcz8zrAWwA8Bkiui5jmUx8CcDrpheJsUgYZv6J589lAHKZUcDM+5l5eu7PVwBckaU8Opj5dWY+nrUcCq4F8NfM/ENmvgDgfwD4bMYyKWHmvwBwOms5TDDze8z83bn//ju0lFvuhoxwiw/m/nTm/uXyNw8ARHQFgBsB/GfTa8VYpAARfZ2ITgC4C/ndWXj5TQDfylqIglIDcMLz97vIoVIrMkS0CsAggFezlSSYObfOYQA/AvACM+dSzjkeBfDbAGZNLxRjEQNE9G0i+n7Av88CADN/hZlXANgF4It5lXPuNV9Ba8u/Kys55+QwyppTKOCx3K4siwYRXQJgD4B7fLv23MDMM3Nu5ysAXEtEV2UtUxBEdBOAHzHzIZvXy1jVGGDmT1u+9L8D2AtgW4LiKDHJSUS/DuAmAJ/ijAtwQlzTvPEugBWev68AcDIjWXoKInLQMhS7mPmZrOUxwcyTRPTnaMWE8phAsAnALUT0qwCWAvhpInqcmb8Q9GLZWSQMEX3U8+ctAI5lJYsOIvoMgN8BcAszT2UtT4H5DoCPEtGVRLQYwD8G8GzGMhUeIiIAfwLgdWb+w6zlUUFEA24mIRFVAHwaOf3NM/N9zHwFM69C6z59UWUoADEWabBzzn3yPQA3oJV5kEf+CMBPAXhhLs33P2YtkAoi+hwRvQvglwDsJaJ9WcvkMpck8EUA+9AKwj7JzBPZSqWGiJ4A8JcAVhPRu0T0z7KWScEmAP8EwPVz9+fhuRVx3rgMwIG53/t30IpZaFNSi4K0+xAEQRCMyM5CEARBMCLGQhAEQTAixkIQBEEwIsZCEARBMCLGQhAEQTAixkIQBEEwIsZCEARBMCLGQhAEQTAixkIQBEEwIsZCEBKCiMaIiInoXwU897W554xzBAQhD0i7D0FICCK6FMA4gJ8F8EvMPD73+KcA7EerwdzHpXGjUATEWAhCghDRJwH8LwBvAvhFAH0AjgBYjpahyG2TQUHwIm4oQUgQZn4ZwO8B+CiAPwbwOICfA/CvxVAIRUJ2FoKQMHOzGL4FYMvcQ08w869lKJIghEZ2FoKQMHNTB7/heejRrGQRhKjIzkIQEmZuWuJ3ATTRilVMALiWmc9lKpgghEB2FoKQIES0BMBuAMvQGl35EIB1kN2FUDDEWAhCsvwBgEEA/5aZ9wPYBuAlAP+ciO7IVDJBCIG4oQQhIYhoCK1YxasAfnluPjeIaAWAwwAWARhk5h9mJ6Ug2CHGQhASgIhWomUQSmgZhDd9z38WwBiA76BlSC6kL6Ug2CPGQhAEQTAiMQtBEATBiBgLQRAEwYgYC0EQBMGIGAtBEATBiBgLQRAEwYgYC0EQBMGIGAtBEATBiBgLQRAEwYgYC0EQBMHI/wfUce4sHyBiLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 1000\n",
    "X = np.random.normal(0,1,n)\n",
    "Y = np.random.normal(0,1,n)\n",
    "plt.xlabel(\"x\", fontsize=20)\n",
    "plt.ylabel(\"Y\", fontsize=20)\n",
    "plt.scatter(X,Y)\n",
    "plt.show(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gráficos de barras:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": [
    "n = 12\n",
    "X = np.arange(n)\n",
    "Y1 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)\n",
    "Y2 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n)\n",
    "plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\n",
    "plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')\n",
    "for x, y in zip(X, Y1): #Mostra os valores\n",
    "    plt.text(x + 0.4, y + 0.05, '%.2f' % y, ha='center', va= 'bottom')\n",
    "plt.ylim(-1.25, +1.25)\n",
    "plt.show(True)       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gráficos de contorno:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "def f(x, y):\n",
    "    return (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 -y ** 2)\n",
    "n = 256\n",
    "x = np.linspace(-3, 3, n)\n",
    "y = np.linspace(-3, 3, n)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "plt.contourf(X, Y, f(X, Y), 8, alpha=.75, cmap='jet')\n",
    "plt.contour(X, Y, f(X, Y), 8, colors='black')\n",
    "plt.show(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grafo de setores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = np.random.uniform(0, 1, 20)\n",
    "plt.pie(Z)\n",
    "plt.show(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos mostras os dados em 3D:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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 mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "X = np.arange(-4, 4, 0.25)\n",
    "Y = np.arange(-4, 4, 0.25)\n",
    "X, Y = np.meshgrid(X, Y)\n",
    "R = np.sqrt(X**2 + Y**2)\n",
    "Z = np.sin(R)\n",
    "ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='hot')\n",
    "plt.show(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogramas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": [
    "data = np.random.randn(100)\n",
    "plt.hist(data);\n",
    "plt.show(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos definir ainda o número de intervalos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": [
    "x = [21,22,23,4,5,6,77,8,9,10,31,32,33,34,35,36,37,18,49,50,100]\n",
    "num_bins = 10\n",
    "n, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ou ainda definir de forma automática:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": [
    "n, bins, patches = plt.hist(x, bins='auto', facecolor='blue', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos mostrar ainda mais de um histograma ao mesmo tempo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAQnUlEQVR4nO3df6zdd13H8eeLzoEM5pBeJms7O7WZNmMyqAMkwUW6pBPSmgihGMwQSENCBfkR2cRMM/9BMCAJC2GOKQoycWK8kuJgjMWYONK7MWDtLDQV1kvn1sEGishsePvHPbccbs+999z23Ps993Ofj6TZ+X6/n937znbzuu++z/f7OakqJEmr3xO6LkCSNBoGuiQ1wkCXpEYY6JLUCANdkhpxVlffeP369bV58+auvr0krUp33333I1U1MehaZ4G+efNmpqamuvr2krQqJfn6fNccuUhSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdJ2W2w8+xO0HH+q6DEl9DHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1IihAj3JjiSHkhxOcs2A669OcjzJvb0/rxt9qZKkhZy12IIk64AbgCuBaWB/ksmqOjhn6d9W1d5lqFGSNIRhOvTLgcNVdaSqHgduAXYtb1mSpKVatEMHNgBH+46ngecNWPcbSV4EfAV4c1UdnbsgyR5gD8CFF1649Gq1KvR/ktH2red3WIm0tgzToWfAuZpz/E/A5qq6FLgd+PCgL1RVN1bVtqraNjExsbRKJUkLGibQp4FNfccbgWP9C6rqm1X1/d7hnwPPHU15kqRhDRPo+4EtSS5KcjawG5jsX5DkmX2HO4H7R1eiJGkYi87Qq+pEkr3AbcA64OaqOpDkemCqqiaBNybZCZwAvgW8ehlrliQNMMybolTVPmDfnHPX9b2+Frh2tKVJkpZiqEDX2uZdK9Lq4KP/ktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRbp+refVvmytp/NmhS1IjDHRJaoSBLkmNMNAlqREGuiQ1wrtcdEb8AGlpfNihS1IjDHRJaoQjl3Fx6FM/fH3xVYufl6Q57NAlqREGuiQ1wkCXpEYY6JLUiKECPcmOJIeSHE5yzQLrXpakkmwbXYmSpGEsepdLknXADcCVwDSwP8lkVR2cs+6pwBuBzy9HoRp/brcrdWuYDv1y4HBVHamqx4FbgF0D1v0x8C7gf0dYnyRpSMME+gbgaN/xdO/cSUkuAzZV1ScX+kJJ9iSZSjJ1/PjxJRcrSZrfMIGeAefq5MXkCcB7gbcu9oWq6saq2lZV2yYmJoavUpK0qGGeFJ0GNvUdbwSO9R0/FbgEuDMJwE8Bk0l2VtXUqAptUv9ToJJ0hobp0PcDW5JclORsYDcwOXuxqr5dVeuranNVbQbuAgxzSVphiwZ6VZ0A9gK3AfcDH6+qA0muT7JzuQuUJA1nqM25qmofsG/OuevmWXvFmZclSVoqnxSVpEa4fe44GuM3S314SBpfduiS1AgDXZIaYaBLUiMMdElqhIEuSY3wLpc1rP+Ole1bz++wEkmjYIcuSY0w0CWpEY5ctKxmxzqzIx3HPNLyMdBXk/4nSC++qrs6JI0lRy6S1Ag79DVk7vhj0LXl/t6Slo8duiQ1wkCXpEYY6JLUCANdkhrhm6IrbYw/vELS6maHLkmNMNAlqREGuiQ1wkCXpEb4puhqN9/+Lu77Iq05duiS1AgDXZIa4chltVrK/ey9teuPPcYjF/zqMhUkqWt26JLUiKECPcmOJIeSHE5yzYDrr0/y5ST3JvnXJFtHX6okaSGLBnqSdcANwFXAVuCVAwL7b6rqWVX1bOBdwHtGXqkkaUHDdOiXA4er6khVPQ7cAuzqX1BV3+k7PAeo0ZUoSRrGMG+KbgCO9h1PA8+buyjJG4C3AGcDA995S7IH2ANw4YUXLrVWSdIChunQM+DcKR14Vd1QVT8LvB34g0FfqKpurKptVbVtYmJiaZVKkhY0TIc+DWzqO94IHFtg/S3AB86kqOa4Za6kFTBMh74f2JLkoiRnA7uByf4FSbb0Hb4E+OroSlSrbj/4kB8eLY3Qoh16VZ1Ishe4DVgH3FxVB5JcD0xV1SSwN8l24P+AR4Grl7NoSdKphnpStKr2AfvmnLuu7/WbRlyXJGmJfFJUkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIa4WeKjlL/JlwXX9VdHZLWJDt0SWqEHfoas/7YHbDuvN7RczqtRdJo2aFLUiMMdElqhIEuSY1whr4G3Xv0sZkXF3Rbx6z+Ty3avvX8DiuRVjc7dElqhIEuSY0w0CWpEc7QW9L/pKqkNccOXZIaYYe+XOyWJa0wA10C7jx657zXrth0xYrVIZ0JRy6S1Ag79MadfIhIK26+rt+OX8vFDl2SGmGHruY4D9daZYeusXL7wYd+ZG8XScMz0CWpEUONXJLsAN4HrANuqqp3zrn+FuB1wAngOPCaqvr6iGsdT6v4fvP1x+44+fqRC361w0okjcKiHXqSdcANwFXAVuCVSbbOWfYFYFtVXQrcCrxr1IVKkhY2TId+OXC4qo4AJLkF2AUcnF1QVZ/rW38X8KpRFimNq4XegJVW2jAz9A3A0b7j6d65+bwWGDiHSLInyVSSqePHjw9fpSRpUcMEegacq4ELk1cB24B3D7peVTdW1baq2jYxMTF8lZKkRQ0zcpkGNvUdbwSOzV2UZDvwDuBXqur7oylPGi1HJGrZMIG+H9iS5CLgG8Bu4Df7FyS5DPggsKOqHh55lVKH/CWg1WLRQK+qE0n2Arcxc9vizVV1IMn1wFRVTTIzYnkK8HdJAB6oqp3LWHe3VvGtipLaNdR96FW1D9g359x1fa+3j7guSdIS+aSoJDXCQJekRhjoktQIt8/VquSdJ9Kp7NAlqRF26ALceVFqgR26JDXCQJekRhjoktQIA12SGmGgS1IjvMtFGiML3V9/xaYrVqwOrU526JLUCANdkhphoEtSI5yh6xQ+NSqtTga6tMLcWEzLxZGLJDXCDn1Yfo7oirOTlZbGDl2SGmGgS1IjDHRJaoQzdI21e48+dvL1szed12El0vizQ5ekRhjoktQIRy4NcTwhrW0GurRKuLWuFuPIRZIaYYfegP5Ri6S1a6gOPcmOJIeSHE5yzYDrL0pyT5ITSV42+jIlSYtZtENPsg64AbgSmAb2J5msqoN9yx4AXg28bTmKVHfcSldaPYYZuVwOHK6qIwBJbgF2AScDvaq+1rv2g2WoUQ2477F/m/faJee94JRztx98iPsec5QkLcUwI5cNwNG+4+neuSVLsifJVJKp48ePn86XkCTNY5hAz4BzdTrfrKpurKptVbVtYmLidL6EJGkew4xcpoFNfccbgWPLU45GxTtfpLVnmA59P7AlyUVJzgZ2A5PLW5YkaakWDfSqOgHsBW4D7gc+XlUHklyfZCdAkl9KMg28HPhgkgPLWbQk6VRDPVhUVfuAfXPOXdf3ej8zoxhpyRa6A0bS8Hz0X5IaYaBLUiPcy2UVG7c7WRydSN0y0LUkhrY0vgx0qQHz7ZW+0D7p7q/eHgN9jPkJRDpTC4W22uObopLUiLXdoR/61A9fX3zV4ufXuPXH7uDc730VgO88/dKOq5E019oOdK1KjqKkwRy5SFIjDHRJaoQjF60agx6kmj3n6EWyQ5ekZhjoktQIRy6L6b+FcQyMy/4t537zSydfewujNB4MdEmncFuA1cmRiyQ1Ym106MM8+TlmoxVJWqq1Eehasnt6j/hLWj3WXqDbia8I3zRt1+ls1auVsfYCXSfZhUtt8U1RSWqEHbqkkfBWx+4Z6I1bK2MV93QZb4b9ymg30NfQm5/jHtq+QSqtDGfoktSIdjv0VWLQp++My34tq9Fin2bkaEYtayvQ19CYZZz0j1RO999zFCOdubYCvQF3PnqQI9/77innH3v0HK542tYOKpK0WgwV6El2AO8D1gE3VdU751x/IvBXwHOBbwKvqKqvjbbUPsPszbIK3Xv0sYFhDnDk+Hc5cnz/Clc0eqfbzZ+JhT7pCBy/qB2LBnqSdcANwJXANLA/yWRVHexb9lrg0ar6uSS7gT8BXrEcBbfgzkd/+J9uvgCX1orTuaXR2yAHG6ZDvxw4XFVHAJLcAuwC+gN9F/BHvde3Au9PkqqqEdY6lvrDWdJoLRTco/56o/5F0MUvnWECfQNwtO94GnjefGuq6kSSbwNPBx7pX5RkD7Cnd/jfSQ6dTtHLaD1zah4T1rU041oXjG9t1rV0XdX20/NdGCbQM+Dc3M57mDVU1Y3AjUN8z04kmaqqbV3XMZd1Lc241gXjW5t1Ld041jbMg0XTwKa+443AsfnWJDkL+AngW6MoUJI0nGECfT+wJclFSc4GdgOTc9ZMAlf3Xr8MuGMtzM8laZwsOnLpzcT3Arcxc9vizVV1IMn1wFRVTQIfAv46yWFmOvPdy1n0MhrXcZB1Lc241gXjW5t1Ld3Y1RYbaUlqg5tzSVIjDHRJaoSBPkCStyWpJOu7rmVWkncn+fckX0ryD0k6fV49yY4kh5IcTnJNl7XMSrIpyeeS3J/kQJI3dV1TvyTrknwhySe7rqVfkvOS3Nr7+bo/yQu6rgkgyZt7/x/vS/KxJE/qsJabkzyc5L6+cz+Z5DNJvtr759O6qm+WgT5Hkk3MbHPwQNe1zPEZ4JKquhT4CnBtV4X0bQdxFbAVeGWScdg57ATw1qr6BeD5wBvGpK5ZbwLu77qIAd4H/HNV/Tzwi4xBjUk2AG8EtlXVJczckNHlzRZ/CeyYc+4a4LNVtQX4bO+4Uwb6qd4L/B4DHozqUlV9uqpO9A7vYuZ5gK6c3A6iqh4HZreD6FRVPVhV9/Re/xczwbSh26pmJNkIvAS4qeta+iU5F3gRM3eqUVWPV9W4bMh/FvDjvWdbnsypz7+smKr6F059tmYX8OHe6w8Dv76iRQ1goPdJshP4RlV9setaFvEaoMvN3wdtBzEWwTkryWbgMuDz3VZy0p8x0yj8oOtC5vgZ4DjwF71x0E1Jzum6qKr6BvCnzPxN+UHg21X16W6rOsX5VfUgzDQTwDM6rmftBXqS23szubl/dgHvAK4b09pm17yDmdHCR7uqkyG3euhKkqcAfw/8blV9ZwzqeSnwcFXd3XUtA5wFPAf4QFVdBnyXMRgd9ObRu4CLgAuAc5K8qtuqxt+a+4CLqto+6HySZzHzw/PFJDAz0rgnyeVV9Z9d1jYrydXAS4EXd/wk7jDbQXQiyY8xE+YfrapPdF1PzwuBnUl+DXgScG6Sj1TVOATUNDBdVbN/k7mVMQh0YDvwH1V1HCDJJ4BfBj7SaVU/6qEkz6yqB5M8E3i464LWXIc+n6r6clU9o6o2V9VmZn7Qn7NSYb6Y3oeMvB3YWVX/03E5w2wHseIy85v4Q8D9VfWeruuZVVXXVtXG3s/Vbma2xhiHMKf38300ycW9Uy/mR7fG7soDwPOTPLn3//XFjMGbtXP0b3lyNfCPHdYCrMEOfRV7P/BE4DO9v0HcVVWv76KQ+baD6KKWOV4I/Bbw5ST39s79flXt67Cm1eB3gI/2fjkfAX6743qoqs8nuRW4h5kR4xfo8FH7JB8DrgDWJ5kG/hB4J/DxJK9l5hfQy7uqb5aP/ktSIxy5SFIjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUiP8HyljChovan7AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = np.random.normal(0, 0.8, 1000) #mu = 0, sigma = 0.8\n",
    "x2 = np.random.normal(-2, 1, 1000)\n",
    "x3 = np.random.normal(3, 2, 1000)\n",
    "\n",
    "kwargs = dict(histtype='stepfilled', alpha=0.3, density=True, bins=40)\n",
    "\n",
    "plt.hist(x1, **kwargs)\n",
    "plt.hist(x2, **kwargs)\n",
    "plt.hist(x3, **kwargs)\n",
    "\n",
    "plt.show(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7 Classes e objetos (Em elaboração)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "https://realpython.com/python3-object-oriented-programming/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usando programação orientada a objetos, podemos definir processos como herança e polimorfismo. Os leitores interessados pode consultar:<r>\n",
    "    https://realpython.com/python3-object-oriented-programming/<br>\n",
    "    https://www.tutorialspoint.com/python/python_classes_objects.htm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 5 Exercícios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1 - Verifique se um número é positivo, negativo ou zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 - Implemente uma função que calcule o fatorial de um número."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 - Calcule a sequencia de Fibonacci."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4 - Calcule os números primos em um intervalo definido."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5 - Implemente uma funçã que calcule a soma de uma sequência de números."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6 - Gere dois vetores aleatórios de maneira independente e mostre os resultados em um scatterplot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7 - Gere dados com distribuição Gaussiana e mostre o respectivo histograma."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8 - Gere uma tabela onde os valores das colunas seguem uma distribuição exponencial com diferentes taxas. Guarde os valores em um arquivo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 Leituras adicionais"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para aprender mais sobre como acessar os índices de vetores e matrizes: http://www.pythoninformer.com/python-libraries/numpy/index-and-slice/"
   ]
  }
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