{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introdução à linguagem Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Francisco Aparecido Rodrigues, francisco@icmc.usp.br.
\n",
"Universidade de São Paulo, São Carlos, Brasil.
\n",
"https://sites.icmc.usp.br/francisco
\n",
"Copyright: Creative Commons"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"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": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_60013/2513383044.py:3: DeprecationWarning: `magic(...)` is deprecated since IPython 0.13 (warning added in 8.1), use run_line_magic(magic_name, parameter_s).\n",
" get_ipython().magic('reset -sf')\n"
]
}
],
"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"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_60013/1054846601.py:6: DeprecationWarning: `magic(...)` is deprecated since IPython 0.13 (warning added in 8.1), use run_line_magic(magic_name, parameter_s).\n",
" get_ipython().magic('reset -sf')\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)",
"Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_ipython\n\u001b[1;32m 6\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mmagic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreset -sf\u001b[39m\u001b[38;5;124m'\u001b[39m) \n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx = \u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[43mx\u001b[49m)\n",
"\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": 5,
"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": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tipo de a: \n",
"Tipo de b: \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": 7,
"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": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 'a', 'palavra']\n"
]
}
],
"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": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 'a', 'palavra', 2.5, (1.5+0.5j), 'Novo elemento']\n"
]
}
],
"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": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 'a', 'palavra', 2.5]\n"
]
}
],
"source": [
"L = [1, 2, 'a', 'palavra', 2.5, 1.5+0.5j]\n",
"L.pop()\n",
"print(L)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Olá', 'Mundo']"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minhaLista=[\"Olá\", \"Mundo\"]\n",
"novaLista=minhaLista.copy()\n",
"minhaLista"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Olá', 'Mundo']"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"novaLista"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Mundo'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"novaLista=minhaLista.pop()\n",
"novaLista"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Olá']"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minhaLista"
]
},
{
"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": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 'a', 'palavra', 2.5, (1.5+0.5j), 0, 9, 3, 'mundo']\n"
]
}
],
"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": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 'a', 'palavra', 2.5, (1.5+0.5j), 1, 2, 'a', 'palavra', 2.5, (1.5+0.5j)]\n"
]
}
],
"source": [
"L = [1, 2, 'a', 'palavra', 2.5, 1.5+0.5j]\n",
"k = 2*L\n",
"print(k)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'olaolaola'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s=\"ola\"\n",
"s3=3*s\n",
"s3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ordenando uma lista:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 3, 4, 5, 6, 8, 9]\n"
]
}
],
"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": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"z = [1, 2, 3, 4, 5, 6]\n"
]
}
],
"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": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"z = [5 7 9]\n"
]
}
],
"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": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"z = [5 7 9]\n"
]
}
],
"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": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[8 9 3]\n"
]
}
],
"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": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x = [0. 0. 0.] y = [1. 1. 1.]\n"
]
}
],
"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": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"N = (3,)\n"
]
}
],
"source": [
"x = [1,2,3]\n",
"x = np.array(x)\n",
"N = np.shape(x)\n",
"print(\"N = \", N)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"O produto escalar de dois vetores:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"27\n"
]
},
{
"data": {
"text/plain": [
"27"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = np.array([1,2,3])\n",
"y = np.array([1,4,6])\n",
"z = np.dot(x,y) \n",
"print(z)\n",
"z2=x@y\n",
"z2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"E o produto vetorial:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-1 0 0]\n"
]
}
],
"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": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n"
]
}
],
"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": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"C = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [-1, -2, -3], [1, 1, 1], [0, 0, 0]]\n"
]
}
],
"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": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A = \n",
" [[1 2 3]\n",
" [4 5 6]\n",
" [7 8 9]]\n",
"B = \n",
" [[10 11 12]\n",
" [13 14 15]\n",
" [16 17 18]]\n",
"C =2*(A + B) = \n",
" [[22 26 30]\n",
" [34 38 42]\n",
" [46 50 54]]\n"
]
}
],
"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": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A = \n",
" [[1 2 3]\n",
" [4 5 6]]\n",
"Numero de linhas: 2 \n",
"Numero de colunas: 3\n"
]
}
],
"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": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1 8 27]\n"
]
}
],
"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": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1 8 27]\n"
]
}
],
"source": [
"n = 3\n",
"x = np.array([1,2,3])\n",
"y = pow(x,n)\n",
"print(y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Criando vetores conforme os matemáticos"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3])"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u=np.array([1,2,3])\n",
"u"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3])"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u.T"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([4, 5, 6])"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v=np.array([4,5,6])\n",
"v"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculando o produto interno de dois vetores $u$ e $v$"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"32"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u@v"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3 Strings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para definirmos uma string:"
]
},
{
"cell_type": "code",
"execution_count": 95,
"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": 96,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello\n"
]
}
],
"source": [
"print(s[0:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podemos realizar operações em strings:"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"HelloWorld\n",
"Hello\tWorld\n"
]
}
],
"source": [
"s = 'Hello'\n",
"t = 'World'\n",
"v = s + t\n",
"print(v)\n",
"print(s + '\\t' + t)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AAAA\n"
]
}
],
"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": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = 'Mundo'\n",
"s in 'Mundo, mundo vasto mundo.'"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"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": 101,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"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": 102,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"O número 2 é par\n"
]
}
],
"source": [
"x = 2\n",
"s = 'O número ' + str(x) + ' é par'\n",
"print(s)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pi é igual a 3.141592653589793\n"
]
}
],
"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": 104,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ACEG\n"
]
}
],
"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": 105,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The product of 20 and 25 is 500\n"
]
}
],
"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": 106,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello Earth\n"
]
}
],
"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": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'hello world'"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'HELLo worlD'.lower()"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'HELLO WORLD'"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = 'HeLLo world'\n",
"s.upper()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Outros comandos úteis:"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'este eh um teste'.count('te')"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'comando'.endswith('do')"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['__add__',\n",
" '__class__',\n",
" '__contains__',\n",
" '__delattr__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__getitem__',\n",
" '__getnewargs__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__init_subclass__',\n",
" '__iter__',\n",
" '__le__',\n",
" '__len__',\n",
" '__lt__',\n",
" '__mod__',\n",
" '__mul__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__rmod__',\n",
" '__rmul__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" 'capitalize',\n",
" 'casefold',\n",
" 'center',\n",
" 'count',\n",
" 'encode',\n",
" 'endswith',\n",
" 'expandtabs',\n",
" 'find',\n",
" 'format',\n",
" 'format_map',\n",
" 'index',\n",
" 'isalnum',\n",
" 'isalpha',\n",
" 'isascii',\n",
" 'isdecimal',\n",
" 'isdigit',\n",
" 'isidentifier',\n",
" 'islower',\n",
" 'isnumeric',\n",
" 'isprintable',\n",
" 'isspace',\n",
" 'istitle',\n",
" 'isupper',\n",
" 'join',\n",
" 'ljust',\n",
" 'lower',\n",
" 'lstrip',\n",
" 'maketrans',\n",
" 'partition',\n",
" 'removeprefix',\n",
" 'removesuffix',\n",
" 'replace',\n",
" 'rfind',\n",
" 'rindex',\n",
" 'rjust',\n",
" 'rpartition',\n",
" 'rsplit',\n",
" 'rstrip',\n",
" 'split',\n",
" 'splitlines',\n",
" 'startswith',\n",
" 'strip',\n",
" 'swapcase',\n",
" 'title',\n",
" 'translate',\n",
" 'upper',\n",
" 'zfill']"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(s)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'esta eh uma sentenca'.find('uma')"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-1"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'esta eh uma sentenca'.find('nada')"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'O mundo é grande.'"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'O mundo é pequeno.'.replace('pequeno', 'grande')"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['w', 'o', 'r', 'l', 'd']\n"
]
},
{
"data": {
"text/plain": [
"'w?o?r?l?d'"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = list('world')\n",
"print(l)\n",
"'?'.join('world')"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['esta', 'eh', 'uma', 'setenca']"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'esta eh uma setenca'.split()"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['www.amazon', 'com', 'br']"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'www.amazon.com.br'.rsplit(sep='.', maxsplit=2)"
]
},
{
"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": 125,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Antonio': 5752, 'Maria': 5578, 'Paulo': 5915}\n",
"5752\n"
]
}
],
"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": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['Antonio', 'Maria', 'Paulo'])"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tel.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"E o valores armazenados:"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_values([5752, 5578, 5915])"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tel.values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dicionários podem armazenar tipos diferentes:"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'a': 1, 'b': 2, 3: 'hello'}\n"
]
}
],
"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": 129,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'abacaxis': 10, 'bananas': 24, 'laranjas': 36}\n"
]
}
],
"source": [
"estoque = {'abacaxis': 10, 'bananas': 24, 'laranjas': 36, 'peras': 12}\n",
"del estoque['peras']\n",
"print(estoque)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'abacaxis': 20, 'bananas': 24, 'laranjas': 36}\n"
]
}
],
"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": 131,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'b': 1, 'a': 3, 'n': 2}\n"
]
}
],
"source": [
"letterCounts = {}\n",
"for letter in \"banana\":\n",
" letterCounts[letter] = letterCounts.get(letter,0) + 1\n",
"print(letterCounts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 Estruturas Condicionais"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"O comando if tem a seguinte estrutura:
\n",
"if condition:
\n",
" if_body
\n",
"else:
\n",
" else_body"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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": 135,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x é par.\n",
"E eu sou professor\n"
]
}
],
"source": [
"x = 4\n",
"if x%2 == 0: # O comando % retorna o resto da divisao\n",
" print('x é par.')\n",
" print('E eu sou professor')\n",
"else:\n",
" print('x é ímpar.') "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Além do comando if, podemos usar o comando elif: para combinar condicionais:"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a is greater than b\n"
]
}
],
"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": {},
"source": [
"Podemos executar o comando em uma única linha:"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a is greater than b\n"
]
}
],
"source": [
"if a > b: print(\"a is greater than b\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ou então:"
]
},
{
"cell_type": "code",
"execution_count": 138,
"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:
\n",
" commands
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Por exemplo, considerando uma lista:"
]
},
{
"cell_type": "code",
"execution_count": 139,
"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": 140,
"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": 144,
"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": 146,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"4\n",
"6\n",
"8\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": 147,
"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": {},
"source": [
"No caso do laço while, a sequência de comandos é repetida equanto a condição for verdadeira:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"while condition:
\n",
" commands
"
]
},
{
"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 # i=i+2"
]
},
{
"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": {},
"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 ):
\n",
" commands
\n",
" 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": 1,
"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(f\"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": 3,
"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",
"#print(f'Antes: x: {x} y: {y}')\n",
"change(x,y)\n",
"print(\"Depois:\", 'x:', x, 'y:', y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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",
" # Se não informar que total é global ele entende como local\n",
" # global total\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": {},
"source": [
"Abrindo um arquivo para escrita:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"myfile = open('myfile.txt', 'w')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Escreve uma linha no arquivo."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"18"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myfile.write('hello text file\\n')\n",
"myfile.write('goodbye text file\\n')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 11,
"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": 12,
"metadata": {},
"outputs": [],
"source": [
"myfile.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Abre o arquivo apenas para leitura."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"myfile = open('myfile.txt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lê a primeira linha do arquivo:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'hello text file\\n'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myfile.readline()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Le a próxima linha:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'goodbye text file\\n'"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myfile.readline()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lê novamente:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'43,44,45\\n'"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myfile.readline()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"''"
]
},
"execution_count": 17,
"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": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.72489409 0.65410266 0.80932631 0.72203823 0.6118466 0.55207558\n",
" 0.72515099 0.11574717 0.69289922 0.9636328 ]\n",
" [0.64498477 0.94621481 0.35996937 0.93385876 0.546905 0.92144589\n",
" 0.65514604 0.31634224 0.7913764 0.29051262]\n",
" [0.02583394 0.98197514 0.39391398 0.27478578 0.45850236 0.84321915\n",
" 0.57366945 0.90716427 0.86445181 0.17298172]\n",
" [0.87966781 0.20561942 0.98552489 0.51214537 0.05069139 0.79768557\n",
" 0.81993226 0.41422126 0.89060799 0.0761504 ]\n",
" [0.42236807 0.1100777 0.32333309 0.21069645 0.34052879 0.6604336\n",
" 0.24309627 0.74745822 0.87938388 0.45215782]\n",
" [0.33188679 0.57527473 0.97687548 0.9830773 0.03792117 0.84876384\n",
" 0.1902131 0.97667189 0.53653784 0.26181627]\n",
" [0.87622052 0.35456834 0.28370044 0.30583391 0.03824734 0.02546246\n",
" 0.51215434 0.54715615 0.08288125 0.42734047]\n",
" [0.99308175 0.72785063 0.274647 0.28988121 0.55429134 0.05083472\n",
" 0.70223004 0.22725938 0.09946884 0.12454813]\n",
" [0.16384399 0.71014139 0.0232601 0.54935743 0.12810696 0.74811308\n",
" 0.07387622 0.38953883 0.72181942 0.78010152]\n",
" [0.91580658 0.86006053 0.42567078 0.38674536 0.33391625 0.18260091\n",
" 0.14073206 0.02897505 0.7854469 0.8519928 ]]\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": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.725 0.654 0.809 0.722 0.612 0.552 0.725 0.116 0.693 0.964]\n",
" [0.645 0.946 0.36 0.934 0.547 0.921 0.655 0.316 0.791 0.291]\n",
" [0.026 0.982 0.394 0.275 0.459 0.843 0.574 0.907 0.864 0.173]\n",
" [0.88 0.206 0.986 0.512 0.051 0.798 0.82 0.414 0.891 0.076]\n",
" [0.422 0.11 0.323 0.211 0.341 0.66 0.243 0.747 0.879 0.452]\n",
" [0.332 0.575 0.977 0.983 0.038 0.849 0.19 0.977 0.537 0.262]\n",
" [0.876 0.355 0.284 0.306 0.038 0.025 0.512 0.547 0.083 0.427]\n",
" [0.993 0.728 0.275 0.29 0.554 0.051 0.702 0.227 0.099 0.125]\n",
" [0.164 0.71 0.023 0.549 0.128 0.748 0.074 0.39 0.722 0.78 ]\n",
" [0.916 0.86 0.426 0.387 0.334 0.183 0.141 0.029 0.785 0.852]]\n"
]
}
],
"source": [
"dt = np.loadtxt('dados.txt',delimiter=',')\n",
"print(dt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6 Gráficos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para graficar os dados, usamos a biblioteca Matplotlib."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x = np.arange(0,2*np.pi, 0.1) #gera valores de 0 a 2*pi exclusivo com passo 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() # os labels não aparecem se não ativar legend()\n",
"plt.show(True)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,\n",
" 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5,\n",
" 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8,\n",
" 3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1,\n",
" 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function plot in module matplotlib.pyplot:\n",
"\n",
"plot(*args, scalex=True, scaley=True, data=None, **kwargs)\n",
" Plot y versus x as lines and/or markers.\n",
" \n",
" Call signatures::\n",
" \n",
" plot([x], y, [fmt], *, data=None, **kwargs)\n",
" plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
" \n",
" The coordinates of the points or line nodes are given by *x*, *y*.\n",
" \n",
" The optional parameter *fmt* is a convenient way for defining basic\n",
" formatting like color, marker and linestyle. It's a shortcut string\n",
" notation described in the *Notes* section below.\n",
" \n",
" >>> plot(x, y) # plot x and y using default line style and color\n",
" >>> plot(x, y, 'bo') # plot x and y using blue circle markers\n",
" >>> plot(y) # plot y using x as index array 0..N-1\n",
" >>> plot(y, 'r+') # ditto, but with red plusses\n",
" \n",
" You can use `.Line2D` properties as keyword arguments for more\n",
" control on the appearance. Line properties and *fmt* can be mixed.\n",
" The following two calls yield identical results:\n",
" \n",
" >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
" >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
" ... linewidth=2, markersize=12)\n",
" \n",
" When conflicting with *fmt*, keyword arguments take precedence.\n",
" \n",
" \n",
" **Plotting labelled data**\n",
" \n",
" There's a convenient way for plotting objects with labelled data (i.e.\n",
" data that can be accessed by index ``obj['y']``). Instead of giving\n",
" the data in *x* and *y*, you can provide the object in the *data*\n",
" parameter and just give the labels for *x* and *y*::\n",
" \n",
" >>> plot('xlabel', 'ylabel', data=obj)\n",
" \n",
" All indexable objects are supported. This could e.g. be a `dict`, a\n",
" `pandas.DataFrame` or a structured numpy array.\n",
" \n",
" \n",
" **Plotting multiple sets of data**\n",
" \n",
" There are various ways to plot multiple sets of data.\n",
" \n",
" - The most straight forward way is just to call `plot` multiple times.\n",
" Example:\n",
" \n",
" >>> plot(x1, y1, 'bo')\n",
" >>> plot(x2, y2, 'go')\n",
" \n",
" - If *x* and/or *y* are 2D arrays a separate data set will be drawn\n",
" for every column. If both *x* and *y* are 2D, they must have the\n",
" same shape. If only one of them is 2D with shape (N, m) the other\n",
" must have length N and will be used for every data set m.\n",
" \n",
" Example:\n",
" \n",
" >>> x = [1, 2, 3]\n",
" >>> y = np.array([[1, 2], [3, 4], [5, 6]])\n",
" >>> plot(x, y)\n",
" \n",
" is equivalent to:\n",
" \n",
" >>> for col in range(y.shape[1]):\n",
" ... plot(x, y[:, col])\n",
" \n",
" - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
" groups::\n",
" \n",
" >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
" \n",
" In this case, any additional keyword argument applies to all\n",
" datasets. Also this syntax cannot be combined with the *data*\n",
" parameter.\n",
" \n",
" By default, each line is assigned a different style specified by a\n",
" 'style cycle'. The *fmt* and line property parameters are only\n",
" necessary if you want explicit deviations from these defaults.\n",
" Alternatively, you can also change the style cycle using\n",
" :rc:`axes.prop_cycle`.\n",
" \n",
" \n",
" Parameters\n",
" ----------\n",
" x, y : array-like or scalar\n",
" The horizontal / vertical coordinates of the data points.\n",
" *x* values are optional and default to ``range(len(y))``.\n",
" \n",
" Commonly, these parameters are 1D arrays.\n",
" \n",
" They can also be scalars, or two-dimensional (in that case, the\n",
" columns represent separate data sets).\n",
" \n",
" These arguments cannot be passed as keywords.\n",
" \n",
" fmt : str, optional\n",
" A format string, e.g. 'ro' for red circles. See the *Notes*\n",
" section for a full description of the format strings.\n",
" \n",
" Format strings are just an abbreviation for quickly setting\n",
" basic line properties. All of these and more can also be\n",
" controlled by keyword arguments.\n",
" \n",
" This argument cannot be passed as keyword.\n",
" \n",
" data : indexable object, optional\n",
" An object with labelled data. If given, provide the label names to\n",
" plot in *x* and *y*.\n",
" \n",
" .. note::\n",
" Technically there's a slight ambiguity in calls where the\n",
" second label is a valid *fmt*. ``plot('n', 'o', data=obj)``\n",
" could be ``plt(x, y)`` or ``plt(y, fmt)``. In such cases,\n",
" the former interpretation is chosen, but a warning is issued.\n",
" You may suppress the warning by adding an empty format string\n",
" ``plot('n', 'o', '', data=obj)``.\n",
" \n",
" Returns\n",
" -------\n",
" list of `.Line2D`\n",
" A list of lines representing the plotted data.\n",
" \n",
" Other Parameters\n",
" ----------------\n",
" scalex, scaley : bool, default: True\n",
" These parameters determine if the view limits are adapted to the\n",
" data limits. The values are passed on to `autoscale_view`.\n",
" \n",
" **kwargs : `.Line2D` properties, optional\n",
" *kwargs* are used to specify properties like a line label (for\n",
" auto legends), linewidth, antialiasing, marker face color.\n",
" Example::\n",
" \n",
" >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)\n",
" >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')\n",
" \n",
" If you specify multiple lines with one plot call, the kwargs apply\n",
" to all those lines. In case the label object is iterable, each\n",
" element is used as labels for each set of data.\n",
" \n",
" Here is a list of available `.Line2D` properties:\n",
" \n",
" Properties:\n",
" agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n",
" alpha: scalar or None\n",
" animated: bool\n",
" antialiased or aa: bool\n",
" clip_box: `.Bbox`\n",
" clip_on: bool\n",
" clip_path: Patch or (Path, Transform) or None\n",
" color or c: color\n",
" dash_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n",
" dash_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n",
" dashes: sequence of floats (on/off ink in points) or (None, None)\n",
" data: (2, N) array or two 1D arrays\n",
" drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
" figure: `.Figure`\n",
" fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
" gid: str\n",
" in_layout: bool\n",
" label: object\n",
" linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
" linewidth or lw: float\n",
" marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`\n",
" markeredgecolor or mec: color\n",
" markeredgewidth or mew: float\n",
" markerfacecolor or mfc: color\n",
" markerfacecoloralt or mfcalt: color\n",
" markersize or ms: float\n",
" markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]\n",
" path_effects: `.AbstractPathEffect`\n",
" picker: float or callable[[Artist, Event], tuple[bool, dict]]\n",
" pickradius: float\n",
" rasterized: bool\n",
" sketch_params: (scale: float, length: float, randomness: float)\n",
" snap: bool or None\n",
" solid_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n",
" solid_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n",
" transform: unknown\n",
" url: str\n",
" visible: bool\n",
" xdata: 1D array\n",
" ydata: 1D array\n",
" zorder: float\n",
" \n",
" See Also\n",
" --------\n",
" scatter : XY scatter plot with markers of varying size and/or color (\n",
" sometimes also called bubble chart).\n",
" \n",
" Notes\n",
" -----\n",
" **Format Strings**\n",
" \n",
" A format string consists of a part for color, marker and line::\n",
" \n",
" fmt = '[marker][line][color]'\n",
" \n",
" Each of them is optional. If not provided, the value from the style\n",
" cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
" the data will be a line without markers.\n",
" \n",
" Other combinations such as ``[color][marker][line]`` are also\n",
" supported, but note that their parsing may be ambiguous.\n",
" \n",
" **Markers**\n",
" \n",
" ============= ===============================\n",
" character description\n",
" ============= ===============================\n",
" ``'.'`` point marker\n",
" ``','`` pixel marker\n",
" ``'o'`` circle marker\n",
" ``'v'`` triangle_down marker\n",
" ``'^'`` triangle_up marker\n",
" ``'<'`` triangle_left marker\n",
" ``'>'`` triangle_right marker\n",
" ``'1'`` tri_down marker\n",
" ``'2'`` tri_up marker\n",
" ``'3'`` tri_left marker\n",
" ``'4'`` tri_right marker\n",
" ``'8'`` octagon marker\n",
" ``'s'`` square marker\n",
" ``'p'`` pentagon marker\n",
" ``'P'`` plus (filled) marker\n",
" ``'*'`` star marker\n",
" ``'h'`` hexagon1 marker\n",
" ``'H'`` hexagon2 marker\n",
" ``'+'`` plus marker\n",
" ``'x'`` x marker\n",
" ``'X'`` x (filled) marker\n",
" ``'D'`` diamond marker\n",
" ``'d'`` thin_diamond marker\n",
" ``'|'`` vline marker\n",
" ``'_'`` hline marker\n",
" ============= ===============================\n",
" \n",
" **Line Styles**\n",
" \n",
" ============= ===============================\n",
" character description\n",
" ============= ===============================\n",
" ``'-'`` solid line style\n",
" ``'--'`` dashed line style\n",
" ``'-.'`` dash-dot line style\n",
" ``':'`` dotted line style\n",
" ============= ===============================\n",
" \n",
" Example format strings::\n",
" \n",
" 'b' # blue markers with default shape\n",
" 'or' # red circles\n",
" '-g' # green solid line\n",
" '--' # dashed line with default color\n",
" '^k:' # black triangle_up markers connected by a dotted line\n",
" \n",
" **Colors**\n",
" \n",
" The supported color abbreviations are the single letter codes\n",
" \n",
" ============= ===============================\n",
" character color\n",
" ============= ===============================\n",
" ``'b'`` blue\n",
" ``'g'`` green\n",
" ``'r'`` red\n",
" ``'c'`` cyan\n",
" ``'m'`` magenta\n",
" ``'y'`` yellow\n",
" ``'k'`` black\n",
" ``'w'`` white\n",
" ============= ===============================\n",
" \n",
" and the ``'CN'`` colors that index into the default property cycle.\n",
" \n",
" If the color is the only part of the format string, you can\n",
" additionally use any `matplotlib.colors` spec, e.g. full names\n",
" (``'green'``) or hex strings (``'#008000'``).\n",
"\n"
]
}
],
"source": [
"help(plt.plot)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podemos graficar os dados usando a função scatterplot:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"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,color='red')\n",
"#plt.plot(X,Y, \"bo\")\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": [
""
]
},
"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": [
""
]
},
"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": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Z = np.random.uniform(0, 1, 20)\n",
"plt.pie(Z)\n",
"plt.show(True)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.39082674, 0.46206254, 0.61482948, 0.13608258, 0.43281091,\n",
" 0.66700925, 0.922615 , 0.19138701, 0.04186614, 0.13133679,\n",
" 0.28409277, 0.56618617, 0.39629747, 0.04106148, 0.79183692,\n",
" 0.95769013, 0.34230742, 0.32415754, 0.78590725, 0.99835513])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Z"
]
},
{
"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": [
""
]
},
"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": null,
"metadata": {},
"outputs": [],
"source": [
"data = np.random.randn(100) #gera 100 valores de aleatorios de 0 a 1 (porcentagem)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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KuAs4Arixqh5P8rmu/XpgC7AO2AW8Cny6f8ljc8hOEx0ire0PuE8rhfv0BpeqgafMJUmN8M5YSWqcQS9JjTtsgz7J15I80T2aYXOSYyZdU19J/jzJ40l+l2RFXxq20OM1VpokNybZm2THpGsZlyQnJ7k3yc7uv7svTLqmvpL8YZKfJnm026e/n3RN43DYBj2wFTijqt4D/Bz48oTrGYcdwMeB+yZdSB+LfLzGSnMTsHbSRYzZfuCLVfUu4Fzgygb+nP4HuKCqzgTOAtZ2VwyuaIdt0FfV3VW1v5t9kNlr/Fe0qtpZVct9R/GhsJjHa6woVXUf8MKk6xinqtpTVY900y8DO5m9833F6h7X8ko3e2T3WfFXrBy2QT/PZ4A7J12E/s+wR2foDSrJauC9wE8mXEpvSY5Ish3YC2ytqhW/T30egfCGl+Q/gHcMaPpKVf2w6/MVZv8X9JZDWduoFrNPDVj0ozM0eUneAnwPuLqqXpp0PX1V1W+Bs7rf7TYnOaOqVvRvK00HfVV98GDtSS4HLgEurBVyQ8FC+9QIH52xQiQ5ktmQv6Wqvj/pesapqn6d5D+Z/W1lRQf9YXvqJsla4EvAR6rq1UnXo9dZzOM1NGFJAtwA7Kyqb0y6nnFIMnXgCrwkfwR8EHhiokWNwWEb9MC1wNHA1iTbk1w/6YL6SvKxJDPA+4A7ktw16ZpG0f1IfuDxGjuB71bV45Otqp8ktwIPAKclmUlyxaRrGoPzgE8CF3R/h7YnWTfpono6Hrg3yWPMHnBsrap/n3BNvfkIBElq3OF8RC9JhwWDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXufwErFIOj57LcvgAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(data)\n",
"plt.show(True)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"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": [
""
]
},
"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": [
""
]
},
"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": "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\n",
"text/plain": [
""
]
},
"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": {},
"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:\n",
" https://realpython.com/python3-object-oriented-programming/
\n",
" https://www.tutorialspoint.com/python/python_classes_objects.htm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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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|