{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Stack Scipy: conjunto de bibliotecas para computação cientifíca\n", "\n", "Assume-se o conhecimento básico da linguagem de programação Python.\n", "\n", "A linguagem de programação Python se consolidou como o principal instrumento para se fazer \"data science\". Grande parte de sua utilidade provém de seu grande e ativo ecossistema de pacotes para execução de tarefas científicas: Numpy para manipulação de dados numéricos, Pandas para manipulação de dados tabulares e rotulados, Scipy como biblioteca de funções científicas, Matplotlib para produção de gráficos de qualidade e Scikit-learn para rotinas de machine learning.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1) Numpy ([página oficial](http://numpy.org/), [referência rápida](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf))\n", "\n", "A biblioteca Numpy é a fundação das operações de todas as outras bibliotecas que se utilizam de suas estruturas de dados (Numpy arrays) e de suas operações numéricas otimizadas (vetorizadas). É uma biblioteca estável, rápida e em contínuo desenvolvimento.\n", "\n", "O principal problema resolvido pelo Numpy é processamento rápido de arrays (vetores ou matrizes).\n", "\n", "Python é uma linguagem de alto nível e por essa razão facilita muito a vida para os seus usuários, que não precisam se preocupar em declarar os tipos das variáveis e nem lidar com as alocações de memória.\n", "\n", "Mas em contra partida, a linguagem Python perde em velocidade e desempenho para outras linguagens de mais baixo nível, como C e Fortran. Porque isso acontece?\n", "\n", "Python é uma linguagem de tipagem dinâmica, você não precisa definir o tipo da variável na sua declaração, o próprio interpretador faz isso pelo usuário. \n", "\n", "Ver os exemplos abaixo:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "soma inteiros: 10\n", "soma strings: bananamaça\n", "soma listas: [1, 2, 3, 4, 5, 6]\n" ] } ], "source": [ "# numeros inteiros\n", "a = 5\n", "b = 5\n", "print('soma inteiros: ', a + b)\n", "\n", "# strings\n", "a = 'banana'\n", "b = 'maça'\n", "print('soma strings: ', a + b)\n", "\n", "# listas\n", "a = [1, 2, 3]\n", "b = [4, 5, 6]\n", "print('soma listas: ', a + b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podemos perceber que a operação '+' funcionou para todos os tipos de dados (números, strings e listas). Apesar disso facilitar a vida do programador, essa facilidade exige que o interpretador do programa verifique o tipo dos dados sempre que for fazer uma operação com eles e isso é um problema quando se lida com muitos dados, pois o programa pode acabar ficando mais lento.\n", "\n", "O numpy fornece a estrutura de dados ndarray (array de n dimensões). Para utiliza-la é necessário declarar o tipo de variável que será armazenada, dessa forma, pode-se construir vetores e matrizes com tipos fixos de dados e evitar o custo de verificação de tipos. Dessa forma, as operações ficam mais rápidas." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0. 0. 0.]\n", "tipo de a \n", "tipo do elemento (dtype) de a \n", "tipo de elemento de b \n" ] } ], "source": [ "import random\n", "import numpy as np\n", "\n", "#criar um array de tamanho 3 preenchido com 0s\n", "a = np.zeros(3)\n", "print(a)\n", "\n", "#tipo de a \n", "print('tipo de a', type(a))\n", "\n", "#numpy arrays são como as listas de python, porém seus elementos são todos do mesmo tipo (dtype)'''\n", "\n", "#tipo do elemento de a\n", "print('tipo do elemento (dtype) de a', type(a[0]))\n", "\n", "#principais dtype: float64 (padrão), int64, bool\n", "\n", "#criar um array de tamanho 3 preenchido com 0s utilizando inteiros\n", "b = np.zeros(3, dtype='float32')\n", "print('tipo de elemento de b', type(b[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Atributos básicos do Numpy Array" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "c ndim 3\n", "c shape (2, 2, 3)\n", "c size 12\n", "c dtype float64\n" ] } ], "source": [ "#array de uma dimensão\n", "a = np.array([1, 2, 3])\n", "\n", "#array de duas dimensões\n", "b = np.array([(1, 2, 3),(4, 5, 6)])\n", "\n", "#array de três dimensões\n", "c = np.array([[(1.5,2,3), (4,5,6)], [(3,2,1), (4,5,6)]], dtype = float)\n", "\n", "print('c ndim', c.ndim)\n", "print('c shape', c.shape)\n", "print('c size', c.size)\n", "print('c dtype', c.dtype)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indexação (acessando elementos do array)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#caso de uma dimensão\n", "a\n", "a[0] #primeiro elemento, em python vetores iniciam em 0\n", "a[2] #ultimo elemento\n", "a[-1] #ultimo elemento, usando indice negativo\n", "\n", "#qual valor de a[-2]?\n", "\n", "#caso de duas dimensões\n", "b[0,0] #elemento da primeira linha e da primeira coluna\n", "b[1,2] #elemento da segunda linha e da terceira coluna\n", "b[1,-1] #elemento da segunda linha e da ultima coluna\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Slice (acessando sub-arrays)\n", "\n", "o slice acontece através da sintaxe x[start:stop:step]\n", " * start: indice onde inicia o slice\n", " * stop: indice onde termina o slice\n", " * step: é o tamanho do passo para percorrer os indices entre \n", "\n", "veja os exemplos abaixo" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.arange(5)\n", "\n", "x[:3] #primeiro tres elementos\n", "x[1:3] #elementos internos \n", "x[:1] #todos os elementos excluindo o primeiro\n", "x[::2] #elemento sim, elemento nao\n", "x[::-1] #ordem inversa\n", "\n", "#caso de duas dimensões, acessando colunas e linhas\n", "b[:, 0] #primeira coluna de b\n", "b[0, :] #primeira linha de b\n", "\n", "#qual o retorno de b[0]?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reshape (alterando o formato do array)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1],\n", " [2],\n", " [3]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#converter o array de uma dimensão com nove elementos em um array de duas dimensões (3x3)\n", "x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\n", "mod_x = x.reshape(3, 3)\n", "mod_x\n", "\n", "#adicionar uma dimensão extra\n", "y = np.array([1, 2, 3])\n", "y\n", "mod_y = y.reshape((3, 1))\n", "mod_y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Operações com arrays\n", "\n", "Outro ponto que torna o uso do numpy vantajoso, são suas operações vetorizadas. A vetorização é uma técnica que permite executar uma operação em vários elementos do array ao mesmo tempo, fazendo com que processamento fique muito mais eficiente. Muito útil para substituir loops onde é necessário aplicar a operação em cada elemento do array. Na biblioteca numpy, as operações aritméticas básicas entre os arrays, são automaticamente vetorizadas, mas também pode-se utilizar um conjunto de funções para realizar essas operações, essas funções são chamadas universal functions (Ufuncs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### loop comum Python" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 352 ms, sys: 0 ns, total: 352 ms\n", "Wall time: 353 ms\n" ] } ], "source": [ "%%time\n", "\n", "n=1000000\n", "soma=0\n", "for i in range(n):\n", " x = random.uniform(0, 1)\n", " soma += x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### loop vetorizado com numpy" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 18.1 ms, sys: 0 ns, total: 18.1 ms\n", "Wall time: 15.3 ms\n" ] } ], "source": [ "%%time\n", "\n", "x = np.random.uniform(0,1,n)\n", "y = np.sum(x**2)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Repare a diferença de tempo de execução! Uma diferença de quase 10 vezes!_ \n", "\n", "A vetorização é uma técnica utilizada para **eliminar loops Python**, já que esses são normalmente muitos lentos para grande volumes de dados.\n", "\n", "No exemplo anterior:\n", " * x**2 é uma operação aritmética regular de exponenciação, como envolve um array o numpy vetoriza essa operação automaticamente\n", "\n", "Ao invés de x**2, poderíamos ter usado a Ufuncs np.exp(), o que produziria o mesmo resultado.\n", "\n", "Segue uma lista de alguns operadores aritméticos e sua Ufunc com descrição.\n", "\n", "| Operador\t| ufunc |\tdescrição\n", "|-----------|-------|--------------------------\n", "| + | np.add |\t Adição (1 + 1 = 2)\n", "| -\t| np.subtract |\t Subtração (3 - 2 = 1)\n", "| -\t| np.negative |\t Negação (-2)\n", "| *\t| np.multiply |\t Multiplicação (2 * 3 = 6)\n", "| /\t| np.divide |\t Divisão (3 / 2 = 1.5)\n", "| **| np.power |\t Exponenciação (2 ** 3 = 8)\n", "| %\t| np.mod |\t Resto (9 % 4 = 1)\n", "\n", "Utilizando as Ufuncs, realize a soma, subtração, divisão, multiplicação e exponenciação das matrizes m1 e m2." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "m1 = np.array([(1, 1, 1),(1, 1, 1), (1, 1, 1)])\n", "m2 = np.array([(3, 3, 3),(3, 3, 3), (3, 3, 3)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No exemplo anterior, utilizamos dois arrays do mesmo tamanho (3, 3). O numpy consegue realizar operações com arrays de diferentes tamanhos, gerando resultados que seguem um regra especifica da biblioteca. A técnica para se vetorizar operações de arrays de diferentes tamanhos se chama Broadcasting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2) Matplotlib ([página oficial](https://matplotlib.org/), [referência rápida](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Matplotlib_Cheat_Sheet.pdf))\n", "\n", "Matplotlib é uma biblioteca gráfica projetada para computação científica. Gera gráficos 2D e 3D em vários formatos (PDF, PNG, JPG...)\n", "\n", "Matplotlib possuí duas APIs, uma voltada para os usuários do Matlab, pois segue o estilo sintaxe do famoso programa, outra voltada para os usuários de Python, pois segue a sintaxe orientada à objetos. Vamos utilizar a sintaxe orientado à objetos, sendo a preferência dos usuários de Python, por explicitar melhor o que está sendo feito." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import numpy as np\n", "\n", "x = np.linspace(0, 10, 100)\n", "y = np.cos(x)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(x, y)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notar os dois objetos retornado pela chamada plt.subplots():\n", "\n", " * fig é uma instência do objeto Figure, o local onde o gráfico é desenhado\n", " * ax é uma instância do objeto AxesSubplot, os eixos que envolvem a figura" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pequenos ajustes" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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cOGD1pc7t3w9Ll1oCyKVG4sYKC2MlYm80znieCFItnIjOq4cc2Kjb+noYPBh69ow6mtQKq4e8RJzxPBGkWmWlXR2F9cIuv4XVQrlcGgiFJeKwFOQylieCVDv5ZFvBSRXmzYs6GhelHTtg/Xro2BHGjo06mvQIS8Q+CWNG80SQDmGj8dy53oMin4WlgTFjLBnkg4oKKCiwwXM+piBjJSURiMgUEXlXRGpE5NYmHr9eRHaKyNLg9vW4x6aLyLrgNj0Z8WScYcNsTMEHH9h87S7/qMauisOr5HzQpYt9/hsarH3EZaSEE4GItAPuBi4HhgHXiMiwJk59UlXLg9v9wXN7Aj8FxgOVwE9FpEeiMWWcdu1s4Q7wATb5qqbGppXo0cMaivNJ2HvIq4cyVjJKBJVAjapuUNXDwBPA1FY+9zLgNVXdpaq7gdeAKUmIKfNUVtp2wQKflTEfLVxo28rK3B070JzRo60qbMMG2Lkz6mhcE5KRCPoBW+Lu1wbHGvsHEVkuIs+IyIA2PhcRuVlEqkSkamc2fpjOOMPma9+zx64OXf44etRmooXYBUE+6dgRystt30sFGSkZiaCpy5vGLaIvAKWqOgp4HXi4Dc+1g6r3qWqFqlaUlJSccLCREYn9CPiXIb+sXm3Tkffta10q81FYPbRggXeYyEDJSAS1wIC4+/2BrfEnqGqdqh4K7v4BOLu1z80pYSJYvNgGFbn8kM/VQqGhQ63hePt22LQp6mhcI8lIBAuBgSJSJiIdgC8DM+JPEJG+cXevBFYH+68Al4pIj6CR+NLgWG469VTo398G2KxcGXU0Lh0OH4YlS2y/oiLaWKJUUADjxtm+d5jIOAknAlWtB76F/YCvBp5S1WoRuV1ErgxO+7aIVIvIMuDbwPXBc3cB/4Elk4XA7cGx3OXVQ/llxQo4dAhKS2220XwWJoJFi7x6KMMUJuNFVHUmMLPRsZ/E7d8G3NbMcx8EHkxGHFlh3Dh49tnYD0S+DCzKV/HVQvmurMzmV9q1y0ZYn3VW1BG5gI8sTreePa0H0ZEjsHx51NG4VDpwwBK+SGwcST4TiVWPhb2oXEbwRBCF8MvgIy1z27Jl1ilg4EBbtcvFEuKiRT6eJoN4IohCOOHYypVw8GC0sbjUCRN9PjcSN3b66VBcDHv3+niaDOKJIAo9esCZZ1r10IoVUUfjUuHAAZt6XMQmmXMmvnoobD9xkfNEEBWvK81tYbXQoEHQtWvU0WSW8LO/ZIlXD2UITwRRGTvWro68eig3hdVC3kh8rP79oU8fm5b63XejjsbhiSA63btb97n6el/PONd4tVDL4ntReYeJjOCJIEr+ZchN8b2FvFqoaeFnf+lSrx7KAJ4IohRWD1VXe/VQLvFqoePr189GWu/bB+vWRR1N3vNEEKVu3az3UH299x7KFfHVQvmyLvGJiK82W7w42licJ4LIhT8W/mXIDcuXe7VQa4UlpiVLfO6hiHkiiFp4VbRypc1U6bJbONOolwaO77TTbLGmjz6y1ctcZDwRRK1nT5uZ8vBhaytw2evQodj04uGKXK55Xj2UMTwRZILw6jG8mnTZadUqGy1eVmajx93xxX/2vXooMp4IMkF4VRTWL7vsFCZyHzvQemecYWNq6upg8+aoo8lbSUkEIjJFRN4VkRoRubWJx78nIquCxetni8jpcY8dFZGlwW1G4+fmhd69rTvdgQOwZk3U0bgTET8w0BNB64nEqtF8PE1kEk4EItIOuBu4HBgGXCMiwxqdtgSoCBavfwb4v3GPHVDV8uB2JfnKq4ey25o1Nhakf39fiaytws/+0qVePRSRZJQIKoEaVd2gqoeBJ4Cp8Seo6puquj+4Ow9bpN7Fi/8y+EjL7OPVQidu4EA4+WRb2P6DD6KOJi8lIxH0A7bE3a8NjjXnJuDluPtFIlIlIvNE5KrmniQiNwfnVe3cuTOxiDNR3752Jfnxxz5Pe7ZpaLAEDp4ITkRBAYwebfteIo5EMhKBNHGsyfKdiHwVqADujDt8mqpWANcCvxKRM5t6rqrep6oVqlpRUlKSaMyZJ74rXfij4rJDTY0l8N694dRTo44mO4XtBP7Zj0QyEkEtMCDufn9ga+OTROQS4EfAlap6KDyuqluD7QZgDpC/l1TxXwavK80e4Y9XebkldNd2w4ZBx46waZMtbu/SKhmJYCEwUETKRKQD8GXgU71/RGQMcC+WBHbEHe8hIh2D/WJgArAqCTFlp7Iym3+org5qa6OOxrWGqlcLJUP79jB8uO17qSDtEk4EqloPfAt4BVgNPKWq1SJyu4iEvYDuBDoDTzfqJjoUqBKRZcCbwB2qmr+JQMTrSrNNba0l7q5dLZG7ExeWiP2zn3aFyXgRVZ0JzGx07Cdx+5c087y/AyOTEUPOKC+Ht9+2q6Ir87c3bdbwaqHkGTnSGo7XrbM2l86do44ob/jI4kwzeDAUFcH770Mu9o7KNfGJwCWmUycYMsSq25YvjzqavOKJINMUFtqVEXgROdN9+KFVDRUVWQJ3ifOec5HwRJCJ/MuQHcL/n5EjLYG7xI0aZdtVq3xa9jTyRJCJhg+3H5YNG2Dv3qijcc3xaqHk697dGt2PHLFk4NLCE0EmKiryutJMt2+fDSQrLIQRI6KOJreEPee8RJw2nggyVXiVGc5o6TLLihWWqMPGfZc84Wd/+XKfdytNPBFkqtGjrTviqlW28pXLLF4tlDqnnGLTdXzyic+7lSaeCDJVOECpvt7rSjPN4cOx/5OwcdMlT/waBV4iTgtPBJnM60oz0+rV1phZWmqNmy75fN6ttPJEkMnCL8OKFV5Xmkm8Wij1ysqgSxcbq/H++1FHk/M8EWSyU06BPn28rjSTNDTEenKFJTaXfAUFsWo3rx5KOU8Emc6rhzLL+vU2D05JiS0m5FLH1yhIG08EmS6+0czrSqMXXp2Gvbpc6gwdCh06wObNsHt31NHkNE8Emc7rSjNH/NoD3j6QevFrFHj1UEp5Ish08XWlPso4Wtu22YywnTvDmU2uqOqSLawa9USQUp4IsoHXlWaG8Mdo1ChL0C71Ro60Krh334UDB6KOJmcl5dMsIlNE5F0RqRGRW5t4vKOIPBk8Pl9ESuMeuy04/q6IXJaMeHJOWFe6aRPs2RN1NPkrTMTeWyh9OneGs86Co0ehujrqaHJWwolARNoBdwOXA8OAa0RkWKPTbgJ2q+pZwC+B/wqeOwxb43g4MAX4XfB6Ll779ra4N3gROSp79sDGjfZ/MXRo1NHkF+85l3LJKBFUAjWqukFVDwNPAFMbnTMVeDjYfwaYJCISHH9CVQ+p6ntATfB6rjGvK41W2D4zdCh07BhtLPkm/OyvXGklA5d0yUgE/YAtcfdrg2NNnhMsdv8R0KuVzwVARG4WkSoRqdqZj0s4hnWla9bAwYNRR5N/4ruNuvTq3dvGbBw4AGvXRh1NTkpGImiqM3XjDu/NndOa59pB1ftUtUJVK0pKStoYYg7o0sV6qhw9aldGLn0OHrQELOKTzEXFJ6FLqWQkglpgQNz9/sDW5s4RkUKgG7Crlc91ofBq1LuRpteqVTYLbFmZzQrr0i++ncAHViZdMhLBQmCgiJSJSAes8XdGo3NmANOD/WnAG6qqwfEvB72KyoCBwIIkxJSb4ieh87rS9AmvQn0QWXRKS6FbNxthXFsbdTQ5J+FEENT5fwt4BVgNPKWq1SJyu4hcGZz2ANBLRGqA7wG3Bs+tBp4CVgGzgH9RVf+Fa05YV7p/P6xbF3U0+aGhwRIvePtAlOKr5bz3UNIlZRyBqs5U1UGqeqaq/jw49hNVnRHsH1TVq1X1LFWtVNUNcc/9efC8war6cjLiyWneeyi9amps9tfevW0mWBcdbydIGR8emW28rjS94ucW8knmojV4sHXd3bIF6uqijianeCLINmGD5a5dXleaaqrePpBJ4ieh8w4TSeWJINuIePVQurz/vs362qWLJWAXPZ93KyU8EWQj/zKkh08yl3lGjLD/i7VrrdOESwr/dGcjrytND68WyjwnnwwDB1pvLh9YmTSeCLKR15Wm3u7dNttrhw4+yVym8Unoks4TQbbyL0NqhaWB4cMt8brMEZbQVq60Ed8uYZ4IstXIkV5Xmkq+JGXm6tUL+veHQ4dsDiiXME8E2Sq+rjQc+eqSY/9+WxGroMASrss83mEiqTwRZDP/MqRGdbUl2LPOsoTrMk/8KGMfWJkwTwTZLGwnqK6GI0eijSWXeLVQ5uvf36qI9u6F996LOpqs54kgm/XqBaedZnWlq1dHHU1uqK+PVbV5IshcIl4iTiJPBNnOvwzJtWaNJdYBAyzRuswVfvaXLPHqoQR5Ish2Y8bYdtkyq9d2iVmyxLZeGsh8YRvOjh3wwQdRR5PVPBFku759oaQEPv4Y1q+POprs1tDgo4mzSUGBr1GQJJ4Isp1IrFTgX4bErF8P+/ZBcTH06xd1NK41vGo0KRJKBCLSU0ReE5F1wbZHE+eUi8hcEakWkeUi8o9xj/1RRN4TkaXBzS/DToTXlSZH+GMyZoyvPZAthg2zkd8bN9q0IO6EJFoiuBWYraoDgdnB/cb2A19T1eHAFOBXItI97vEfqGp5cPO0fiLCNQrq6nyNghOl+ulE4LJDhw42Iyl4qSABiSaCqcDDwf7DwFWNT1DVtaq6LtjfCuwAShJ8XxevoMDnHkqUrz2QvcLEHTb0uzZLNBH0UdVtAMG2d0sni0gl0AGIb9X8eVBl9EsR6djCc28WkSoRqdq5c2eCYeeg8MuweHG0cWSr+N5CvvZAdomfd+vjj6OOJisd9xMvIq+LyMomblPb8kYi0hd4FLhBVcN+jrcBQ4BxQE/gh809X1XvU9UKVa0oKfECxTEGD4aTToKtW2H79qijyT7ebTR7depkU4XHLy3q2uS4iUBVL1HVEU3cnge2Bz/w4Q/9jqZeQ0S6Ai8BP1bVeXGvvU3NIeAhoDIZf1ReKiyMdaXzInLb7NhhVUMnnQRDhkQdjTsRXj2UkETLwDOA6cH+dOD5xieISAfgL8Ajqvp0o8fCJCJY+4IvOZSIsWNt61+Gtgmr00aNsoTqss/o0dbTa/VqOHgw6miyTqKJ4A5gsoisAyYH9xGRChG5PzjnS8BE4Pomuok+JiIrgBVAMfCfCcaT34YPt14UGzfCrl1RR5M9wkQQJlKXfbp2tZHG8XNFuVZL6PJHVeuASU0crwK+Huz/CfhTM8+/OJH3d420b29d6RYvtlLBpGP+a1xjdXW2JGXHjrHlP112GjMG1q2zz/64cVFHk1W8e0Su8eqhtgn/nUaM8CUps13YTrByJRw+HG0sWcYTQa4ZOdLquWtqbK521zKvFsodPXvC6afb7LHV1VFHk1U8EeSaoiIbdh8/UtY17aOPYMMGS5zh6FSX3c4+27aLFkUbR5bxRJCLwiKyfxlaFs7NNHy4JVCX/cJEsHy5r9rXBp4IclF5ObRrZwuw79sXdTSZK2wf8Gqh3FFc7NVDJ8ATQS7q1ClWPeSNxk3bu9cSZbt2sYF4LjeEid1LxK3miSBXeV1pyxYvjlULdeoUdTQumbx6qM08EeSq0aO9eqglYYKsqIg2Dpd8JSW25vTBg7BqVdTRZAVPBLnKq4eat2ePDTwqLIxN3+1yi5eI28QTQS4Lr3arqqKNI9N4b6HcFyaCZcu8eqgVPBHkslGjrHpo7VofXBYvTIw+DUHu6t07Vj3kvYeOyxNBLuvUya56VX3BmtDu3Tbqun17G4XtcleY6BcujDaOLOCJINeFRWSvHjJhQhwxwquFcl1YNbpsmY0rcM3yRJDrysvt6nfdOp+aGmIJ0XsL5b5eveDMM62NwKdbaZEnglxXVBTrGZPvReSdO21uoY4dvVooX3j1UKsklAhEpKeIvCYi64Jtj2bOOxq3KM2MuONlIjI/eP6TwWpmLtkqgxVAFyyINo6ohT8G5eWWDFzuO/tsW7msuho++STqaDJWoiWCW4HZqjoQmB3cb8oBVS0PblfGHf8v4JfB83cDNyUYj2tKOHq2tha2bYs6mmiowvz5tl/pS2Pnja5dbR3qhgbvMNGCRBPBVODhYP9hbN3hVgnWKb4YeOZEnu/aoLAwNiNpvpYKtmyBDz6ALpFWxE8AABBtSURBVF1soJ3LH2Hi9+qhZiWaCPqo6jaAYNu7mfOKRKRKROaJSPhj3wvYo6r1wf1aoF+C8bjmxFcPqUYbSxTC0kBFBRR401heKS+3i6G1a21UuTvGcb8RIvK6iKxs4ja1De9zmqpWANcCvxKRMwFp4rxmf6FE5OYgmVTt3LmzDW/tABg0CLp1gw8/tMXt80lDQ+xq0KuF8k+nTtZdWDV/S8THcdxEoKqXqOqIJm7PA9tFpC9AsN3RzGtsDbYbgDnAGOBDoLuIFAan9Qe2thDHfapaoaoVJSUlbfgTHWBXwWEPivDqOF+sXWurkZWUQFlZ1NG4KJxzjm3nzs3PEvFxJFpGngFMD/anA883PkFEeohIx2C/GJgArFJVBd4EprX0fJdE48fbdsECqK9v+dxcEt9ILE0VRF3OGzkSTj4Ztm61ThPuUxJNBHcAk0VkHTA5uI+IVIjI/cE5Q4EqEVmG/fDfoarh3LA/BL4nIjVYm8EDCcbjWjJgAJx6qnWjW7Ei6mjS4/DhWG8RrxbKX4WFsRLx3LnRxpKBEkoEqlqnqpNUdWCw3RUcr1LVrwf7f1fVkao6Otg+EPf8DapaqapnqerVqurjwFNJBM47z/bz5cuwZIlNPFZWBqecEnU0LkrnnmvbBQvg6NFoY8kw3n0i34wfb+0FK1bkx4I1f/+7bcME6PLX6afbxcC+fT4jaSOeCPJN1642wKyhIfd7UNTVwZo1NteSTzntRGKNxvPmRRtLhvFEkI/ypXoo/PvGjIGTToo2FpcZzjnHEsKyZbB/f9TRZAxPBPlo1CjrW71li91ykapXC7lj9egBgwdbr7lcLxG3gSeCfFRYGOtBk6ulgrVrrWoo/OI7Fzr/fNv+9a8+piDgiSBfhVfJ8+bl5pquYWng3HN9Sgn3aWPG2JiC2lrYtCnqaDKCf0Py1Wmn2e2TT3JvVsb9+2HRItv3aiHXWGFhrCvpX/8abSwZwhNBvhKBiRNt/+23o40l2cJSzpAhNq2Ec42F1UMLF9o4kzzniSCfjRtnC7TU1NjQ+1ygGktsF14YbSwuc/XtCwMH2lrGPj21J4K8VlQUm38oV0oF69bZ4jvdusWW6HSuKRdcYFuvHvJEkPfC6qF582xenmz31lu2veACaNcu2lhcZhs71rpRb9qUu92oW8kTQb4bMABKS+HAAaiqijqaxOzdaw3fBQWxOmDnmtO+fazR+M03o40lYp4IXKxUMGdOdver/tvfbOqMUaNs/IBzx3PRRdZxYv78/Jh7qxmeCJw1Gp98shWR16+POpoT09DgjcSu7Xr3trUK6uvzuq3AE4GDDh1iP56vvx5tLCdq8WLYvdu+2EOHRh2NyyYXX2zbOXPya8GmOJ4InLnoImtcXbrU1jXOJqrw2mu2P3myr0Lm2mbIEFuw6aOPcm9wZSsllAhEpKeIvCYi64LtMRWzIvIZEVkadzsoIlcFj/1RRN6Le6w8kXhcArp1syoiVXjjjaijaZuaGti4ETp3jk0z7FxricRKBbNnRxtLRBItEdwKzFbVgcDs4P6nqOqbqlququXAxcB+4NW4U34QPq6qSxOMxyXikkts+7e/WS+ibPFq8HG66CKr5nKurcaPt3ayjRthw4aoo0m7RBPBVODhYP9h4KrjnD8NeFlVfSLwTDRggM3UeeiQJYNs8MEHsHy5dQW86KKoo3HZqkOHWO+5WbOijSUCiSaCPqq6DSDY9j7O+V8GHm907OcislxEfikiHZt7oojcLCJVIlK1c+fOxKJ2zQtLBa+/nh0NZ2Hj9rnnQpcu0cbistukSXZBsWyZzUyaR46bCETkdRFZ2cRtalveSET6AiOBV+IO3wYMAcYBPYEfNvd8Vb1PVStUtaLEJxJLnZEjoV8/2LMn80sFH31k6ymIxBKYcyeqS5dYqeDll6ONJc2OmwhU9RJVHdHE7Xlge/ADH/7Q72jhpb4E/EVV/2fye1XdpuYQ8BBQmdif4xImAp/7nO3PmpXZpYKXX7b4xoyBPn2ijsblgsmTbZrqRYtszqo8kWjV0AxgerA/HXi+hXOvoVG1UFwSEax9YWWC8bhkGDPGutPt3g3vvBN1NE3btcsGAInA5z8fdTQuV/ToYWtYqOZVW0GiieAOYLKIrAMmB/cRkQoRuT88SURKgQHAW42e/5iIrABWAMXAfyYYj0uG+FJBeNWdaWbOtLgqKixpOZcsU6bYfFULFkCetEcmlAhUtU5VJ6nqwGC7Kzhepapfjztvo6r2U9WGRs+/WFVHBlVNX1XVjxOJxyXR2LGxUkG47GOm+PBDK6l4acClQq9eNh6loQFeeCHqaNLCRxa7ponAFVfY/syZmTVF9Usv2Zd0/HhvG3CpccUV1lYwf35erGvsicA17+yzbWzB7t2xQVtR27bNegoVFMSqr5xLtuLi2GjjZ57JjFl5jx5N2fQvnghc80TgH//R9mfNsoQQJVV48knbnn++r0fsUuvyy2208dq1NmgxarNnw89+lpLVBD0RuJYNHGjtBUeOwLPPRhvLkiWwerV9Oa863iB25xLUqVOs1PnnP9sVeVR27YIXX7TvYa9eSX95TwTu+KZNs/rSBQuim4fl8GF46inbv+oqSwbOpdrEiTa1+fbtsWVQo/Dkkzb1y9ixMHx40l/eE4E7vl694NJLbf+JJ6yhNt1eftmqpgYM8GUoXfoUFtqFEMBzz0FdXfpjWL7cpofv2DFWVZtknghc60yZYoNtNm2CV145/vnJtH17rLH6mmusodi5dBk92q7EDx2CRx9Nb8PxoUN28QUwdSp0756St/FvlGudjh1hejCI/IUXYMuW9LxvfT3cf79tzzsPzjwzPe/rXLxrr7XqyNWr0zsH1wsvWClkwAD4zGdS9jaeCFzrDR1qUz0fPQoPPpieEcfPPw+bN1v11Je+lPr3c64pXbpYaRSsO2k6etCtXGkr7xUUwFe/mtKSsCcC1zZf/KI1nm3daj/SqbR6tVUJFRTATTfBSSel9v2ca0lFhVUTHTwIDzyQ2guh3bvtYgusSqi0NHXvhScC11YdO8INN9gYg1dftVkaU2HfPnjoIdu/4gqvEnLRE7Er8+7dYd06q7tPRXvB0aPwhz/AJ59YD6HLLkv+ezTiicC13RlnWMkA7Kpl/frkvv7Bg3DXXbbewJlnwmc/m9zXd+5Ede0K3/ym9Sb661+T36VU1cYsrF9vCSe86EoxTwTuxEyebH2s6+vhd7+DHS0tRdEG9fVwzz3WO6m4GG65xXsJucxSWhrrOPHkk1aFmSwzZtgI4oIC+PrX07bqnn/D3IkRscaz4cPh44/tCj7ReVAaGqyEsWaNXXl997u2dS7TVFZal+qGBrj77uRMQTFzpt0KCuCf/slG9aeJJwJ34goK4OabrWvbjh1wxx0nXk30ySfw299am0NREXznOz6XkMtsU6fCBRfYtA/33HPiiziF010//7xdYN1wg41bSCNPBC4xRUXw/e/DsGHWwPvf/w3z5rWtEW3zZvj5z6G62vpq/+u/Qv/+qYvZuWQoKICvfMU6MzQ0wCOPwF/+0rYp23fvhl/+0uYRAvja16y0kWaiCbR6i8jVwM+AoUClqlY1c94U4NdAO+B+VQ1XMisDnsAWrl8MXKeqx/1XrKio0KqqJt/KRaWhwepL58yx+4MG2ZxALfX22bMH3njD6kTr6+H0061NoGfPtITsXNLMmRPrRdSzJ1x9tS352lxD7+HDsHChjUnYv9/aAq6/HkaMSGmYIrJIVSuOOZ5gIhgKNAD3Av+rqUQgIu2AtdhSlrXAQuAaVV0lIk8Bz6rqEyLye2CZqt5zvPf1RJDB3n7bror277f7Q4fC4MH2I19cbD/+H35oU/suWBCb0fGCC2welfbto4vduUTU1FgyCEfd9+tnJeWhQy057N9vVaDV1bbgzYEDdt6IEdb4nIb2sJQkgrgXn0PzieBc4Geqellw/7bgoTuAncApqlrf+LyWeCLIcPv3w+uv2+3QoebPE7G60EsvTfmAGefSoqHBpqB47jn70W9JaamN1D/nnLR0EYXmE0FhGt67HxA/MU0tMB7oBexR1fq44/2aexERuRm4GeC0005LTaQuOTp1giuvtLlRVqywNoDNm21O9R49rGTQu7d9AbxB2OWSggLrVn3uudZxYs0au+3fb+1fnTrBKafY4xnUDnbcRCAirwOnNPHQj1S1NXMMNJXqtIXjTVLV+4D7wEoErXhfF7UuXWyiuPPOizoS59KrfXsYMsRuWeC4iUBVL0nwPWqBAXH3+wNbgQ+B7iJSGJQKwuPOOefSKB3dRxcCA0WkTEQ6AF8GZqg1TrwJBKs+MB1I8SxmzjnnGksoEYjIF0SkFjgXeElEXgmOnyoiMwGCq/1vAa8Aq4GnVLU6eIkfAt8TkRqszeCBROJxzjnXdknpNZRu3mvIOefarrleQz6y2Dnn8pwnAuecy3OeCJxzLs95InDOuTyXlY3FIrIT2HSCTy/GxjDkE/+b84P/zbkv0b/3dFU9Zjh/ViaCRIhIVVOt5rnM/+b84H9z7kvV3+tVQ845l+c8ETjnXJ7Lx0RwX9QBRMD/5vzgf3PuS8nfm3dtBM455z4tH0sEzjnn4ngicM65PJdXiUBEpojIuyJSIyK3Rh1PKonIABF5U0RWi0i1iHwn6pjSRUTaicgSEXkx6ljSQUS6i8gzIrIm+P8+N+qYUk1E/i34XK8UkcdFpCjqmJJNRB4UkR0isjLuWE8ReU1E1gXbHsl4r7xJBCLSDrgbuBwYBlwjIsOijSql6oHvq+pQ4BzgX3L87433HWzK83zxa2CWqg4BRpPjf7uI9AO+DVSo6gigHbbOSa75IzCl0bFbgdmqOhCYHdxPWN4kAqASqFHVDap6GHgCmBpxTCmjqttUdXGwvw/7cWh2TehcISL9gSuA+6OOJR1EpCswkWAtD1U9rKp7oo0qLQqBk0SkEOhEDq5uqKpvA7saHZ4KPBzsPwxclYz3yqdE0A/YEne/ljz4YQQQkVJgDDA/2kjS4lfA/wYaog4kTc4AdgIPBdVh94vIyVEHlUqq+j7w/4DNwDbgI1V9Ndqo0qaPqm4Du9gDeifjRfMpEUgTx3K+76yIdAb+DHxXVfdGHU8qicjngB2quijqWNKoEBgL3KOqY4BPSFJ1QaYK6sWnAmXAqcDJIvLVaKPKbvmUCGqBAXH3+5ODxcl4ItIeSwKPqeqzUceTBhOAK0VkI1b1d7GI/CnakFKuFqhV1bC09wyWGHLZJcB7qrpTVY8AzwLnRRxTumwXkb4AwXZHMl40nxLBQmCgiJSJSAescWlGxDGljIgIVm+8WlX/O+p40kFVb1PV/qpaiv3/vqGqOX2lqKofAFtEZHBwaBKwKsKQ0mEzcI6IdAo+55PI8QbyODOA6cH+dOD5ZLxoYTJeJBuoar2IfAt4Betl8KCqVkccVipNAK4DVojI0uDY/1HVmRHG5FLjX4HHggucDcANEceTUqo6X0SeARZjveOWkINTTYjI48BFQLGI1AI/Be4AnhKRm7CEeHVS3sunmHDOufyWT1VDzjnnmuCJwDnn8pwnAuecy3OeCJxzLs95InDOuTznicA55/KcJwLnnMtz/x9BdbFBwQW5KQAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#alterando espessura das linhas, cor, transparência e adicionando legenda\n", "fig, ax = plt.subplots()\n", "ax.plot(x, y, 'r-', linewidth=2, label='coseno', alpha=0.6)\n", "ax.legend()\n", "plt.show()\n", "\n", "#incluindo string Latex para formatação matemática\n", "fig, ax = plt.subplots()\n", "ax.plot(x, y, 'b--', linewidth=2, label='$y=\\cosine(x)$')\n", "ax.legend()\n", "ax.set_yticks([-1, 0, 1]) #alterando a marcação do eixo y\n", "ax.set_title('Exemplo') #incluindo título\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiplos gráficos em um mesmo eixo" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y1 = np.cos(x)\n", "y2 = np.sin(x)\n", "y3 = np.log(x+1)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(x, y1, linewidth=1, label='coseno')\n", "ax.plot(x, y2, '--', linewidth=1, label='seno')\n", "ax.plot(x, y3, 'r', linewidth=1, label='logaritmo')\n", "ax.legend()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multíplos gráficos" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.linspace(0, 10, 100)\n", "y4 = np.random.randint(20, size=100)\n", "\n", "fig, axes = plt.subplots(3, 2, figsize=(10, 12))\n", "\n", "#gráfico 1\n", "axes[0,0].plot(x, y4)\n", "axes[0,0].set_title('line plot')\n", "\n", "#gráfico 2\n", "axes[1,0].scatter(x, y4, color='r')\n", "axes[1,0].set_title('scatter plot')\n", "\n", "#gráfico 3\n", "axes[2,0].bar(x[::5], y4[::5], color='green', edgecolor='black') #notar o uso do slice [::5]\n", "axes[2,0].set_title('bar plot')\n", "\n", "#gráfico 1\n", "axes[0,1].plot(x, y4)\n", "axes[0,1].set_title('line plot')\n", "\n", "#gráfico 2\n", "axes[1,1].scatter(x, y4, color='r')\n", "axes[1,1].set_title('scatter plot')\n", "\n", "#gráfico 3\n", "axes[2,1].bar(x[::5], y4[::5], color='green', edgecolor='black') #notar o uso do slice [::5]\n", "axes[2,1].set_title('bar plot')\n", "\n", "\n", "plt.show()\n", "#se quiser salvar o gráfico gerado, basta descomentar o comando abaixo\n", "#fig.savefig('nome_da_figura.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3) Scipy ([página oficial](https://www.scipy.org/), [referência rápida](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_SciPy_Cheat_Sheet_Linear_Algebra.pdf))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assim como o Numpy, o Scipy é uma biblioteca estável, madura e amplamente utilizada. Suas rotinas encapsulam códigos de bibliotecas numéricas do Fortran consagradas como LAPACK.\n", "\n", "Enquanto o Numpy realiza armazenamento e operações entre arrays de maneira eficiente, o Scipy é um pacote que contém várias ferramentas construídas sobre o código Numpy, utilizando esses arrays e suas operações. Portanto, para utilizar o Scipy, é necessário ter instalado o Numpy.\n", "\n", "Não há muito o que aprender sobre o Scipy, pois são inúmeras ferramentas de computação científicas. Vamos mostrar dois pacotes dos mais utilizados: linalg, para algebra linear (matrizes e vetores) e stats, para estatísticas nos arrays." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linalg \n", "\n", "Repare que scipy.linalg contém e expande numpy.linalg\n", "\n", "Utilizando o scipy, resolver o sistema linear abaixo:\n", "\n", "$$\\begin{eqnarray}\n", "4x - y - 3z = 15 \\\\\n", "3x - 2y + 5z = -7 \\\\\n", "2x + 3y + 4z = 7 \\\\\n", "\\end{eqnarray}$$\n", "\n", "Em notação de matriz\n", "\n", "$$\n", "\\begin{bmatrix}\n", "4 & -1 & -3 \\\\\n", "3 & -2 & 5 \\\\\n", "2 & 3 & 4 \\\\\t\t\n", "\\end{bmatrix}\n", "\\cdot\n", "\\begin{bmatrix}\n", "x \\\\\n", "y \\\\\n", "z \\\\\t\t\t\n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", "15 \\\\\n", "-7 \\\\\n", "7 \\\\\n", "\\end{bmatrix}\n", "$$\n", "\n", "Vamos chamar a matrix de coeficientes de A\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x=3.909090909090909, y=-1.0000000000000002, z=0.5454545454545455\n" ] }, { "data": { "text/plain": [ "array([15., -7., 7.])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy import linalg\n", "\n", "A = np.array([[4, -1, -3], [-3, -2, 5], [2, 3, 4]])\n", "b = np.array([15, -7, 7])\n", "\n", "sol = np.linalg.solve(A, b)\n", "print(\"x={0}, y={1}, z={2}\".format(*sol))\n", "\n", "#verificando o resultado\n", "np.dot(A, sol)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Stats\n", "\n", "O pacote scipy.stats fornece ferramentas para lidar com: \n", " * variáveis aleatórias (densidades, distribuições acumulativas, reamostragem)\n", " * estimação\n", " * testes estatísticos\n", " \n", "Exemplo: plotar um histograma de dados com comportamento gaussiano e sua distribuição acumulada" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from scipy.stats import norm, cumfreq\n", "from random import uniform\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "#curva normal com média 0 e desvio padrao 1\n", "curve = norm(0, 1)\n", "\n", "#gerando 100 observações com curva normal\n", "x = curve.rvs(100)\n", "\n", "fig, ax = plt.subplots()\n", "ax.hist(x, alpha=0.6, bins=20) #histograma com 20 casas\n", "ax.set_ylabel(\"Numero de observações\")\n", "\n", "ax2 = ax.twinx() #copia de eixo para mudar escala e cor\n", "ax2.hist(x, cumulative=True, bins=20, histtype='step', color='red', linewidth=2)\n", "ax2.set_ylabel(\"Total acumulado\", color='red')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4) Pandas ([página oficial](https://pandas.pydata.org/), [referência rápida](https://assets.datacamp.com/blog_assets/PandasPythonForDataScience.pd))\n", "\n", "Pandas é um pacote com diversas ferramentas eficientes para análise de dados. Assim como o Scipy, Pandas utiliza as estruturas e operações dos arrays do Numpy, que funcionam muito bem para cálculos numéricos, mas também os expande, dando mais flexibilidade com a possibilidade de usar eixos e indices rotulados, procedimentos para lidar com conjuntos de dados incompletos (missing data) e rotinas para facilitar a leitura de dados em formatos consagrados (CSV, Excel, SQL ...). \n", "\n", "As duas estruturas de dados mais importantes do Pandas são: Series e DataFrame.\n", "\n", "### Series\n", "Pode-ser pensar que é um array unidimensional. Toda Series possui um indice (index), ele pode ser usado para rotular cada elemento desse array, funcionando de maneira muito parecida com um dicionário Python. \n", "\n", "Obs: se os seus dados possuem um eixo de tempo, escolher esse eixo como index facilita muito a manipulação e análise de dados. O Pandas é uma ferramenta extremamente poderosa para se trabalhar com séries temporais." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 40\n", "1 48\n", "2 53\n", "3 42\n", "4 55\n", "5 38\n", "6 29\n", "7 31\n", "8 48\n", "9 44\n", "Name: idades, dtype: int64\n", "2 53\n", "3 42\n", "4 55\n", "Name: idades, dtype: int64\n", "40\n", "count 10.000000\n", "mean 42.800000\n", "std 8.625543\n", "min 29.000000\n", "25% 38.500000\n", "50% 43.000000\n", "75% 48.000000\n", "max 55.000000\n", "Name: idades, dtype: float64\n", "Jessica 40\n", "Vanessa 48\n", "Roberto 53\n", "Saulo 42\n", "Fabio 55\n", "Juliana 38\n", "Tatiana 29\n", "Jose 31\n", "Eliane 48\n", "Tomas 44\n", "Name: idades, dtype: int64\n", "idade de Fabio é 55\n" ] }, { "data": { "text/plain": [ "Index(['Jessica', 'Vanessa', 'Roberto', 'Saulo', 'Fabio', 'Juliana', 'Tatiana',\n", " 'Jose', 'Eliane', 'Tomas'],\n", " dtype='object')" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "#criar um array ficticio\n", "idade = [40, 48, 53, 42, 55, 38, 29, 31, 48, 44]\n", "\n", "#criando um objeto Series\n", "s = pd.Series(idade, name='idades')\n", "print(s) #nesse exemplo, os indices são numerais 0, 1, 2....\n", "\n", "#os slices funcionam como no numpy\n", "print(s[2:5])\n", "print(s[0])\n", "\n", "#funções estatísticas embutidas\n", "s.mean()\n", "s.max()\n", "s.min()\n", "\n", "print(s.describe()) #resumo estatistico\n", "\n", "#adicionando index rotulado\n", "s.index = ['Jessica', 'Vanessa', 'Roberto', 'Saulo', 'Fabio', 'Juliana', 'Tatiana', 'Jose', 'Eliane', 'Tomas']\n", "print(s)\n", "\n", "#pode-se utilizar os rótulos para acessar os elementos correspondendes\n", "print('idade de Fabio é', s['Fabio'])\n", "\n", "#acessar os valores \n", "s.values\n", "\n", "#acessar o index\n", "s.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dataframe\n", "DataFrame é uma generalização de Series, pois permite a inclusão de diversas colunas. Pode ser pensando como uma planilha de Excel extremamente eficiente. É uma excelente ferramenta para analisar dados tabulares (organizados em linhas e colunas). \n", "\n", "Vamos utiliza-lo para abrir o arquivo dados_saude_cancer.csv (dados fictícios apenas para ilustrar os exemplos)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Nome Idade Colesterol Anos fumante Atividade física Ingestão alcool \\\n", "0 Jessica 40 44 13 Não Sim \n", "1 Vanessa 48 55 0 Não Não \n", "2 Roberto 53 65 30 Não Sim \n", "3 Saulo 42 38 15 Sim Sim \n", "4 Fabio 55 52 10 Sim Sim \n", "5 Juliana 38 44 0 Não Não \n", "6 Tatiana 29 60 0 Sim Não \n", "7 Jose 31 75 10 Não Sim \n", "8 Eliane 48 55 0 Sim Não \n", "9 Tomas 44 50 22 Não Não \n", "\n", " Histórico familiar Nível de glicose \n", "0 Não 100 \n", "1 Não 90 \n", "2 Sim 110 \n", "3 Sim 105 \n", "4 Não 98 \n", "5 Sim 92 \n", "6 Não 105 \n", "7 Não 90 \n", "8 Não 92 \n", "9 Não 97 \n" ] } ], "source": [ "df = pd.read_csv('data/dados_saude_cancer.csv')\n", "type(df)\n", "\n", "#o arquivo é uma tabela, essa tabela é representada no formato linha X colunas\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Idade Colesterol Anos fumante Nível de glicose\n", "count 10.000000 10.000000 10.000000 10.00000\n", "mean 42.800000 53.800000 10.000000 97.90000\n", "std 8.625543 10.932114 10.424331 7.04667\n", "min 29.000000 38.000000 0.000000 90.00000\n", "25% 38.500000 45.500000 0.000000 92.00000\n", "50% 43.000000 53.500000 10.000000 97.50000\n", "75% 48.000000 58.750000 14.500000 103.75000\n", "max 55.000000 75.000000 30.000000 110.00000" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe() #resumo estatistico das variáveis numéricas" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Nome Idade Colesterol Anos fumante Atividade física Ingestão alcool \\\n", "2 Roberto 53 65 30 Não Sim \n", "3 Saulo 42 38 15 Sim Sim \n", "4 Fabio 55 52 10 Sim Sim \n", "\n", " Histórico familiar Nível de glicose \n", "2 Sim 110 \n", "3 Sim 105 \n", "4 Não 98 \n", " Nome Idade Anos fumante\n", "0 Jessica 40 13\n", "1 Vanessa 48 0\n", "2 Roberto 53 30\n", "3 Saulo 42 15\n", "4 Fabio 55 10\n", "5 Juliana 38 0\n", "6 Tatiana 29 0\n", "7 Jose 31 10\n", "8 Eliane 48 0\n", "9 Tomas 44 22\n", " Nome Idade Colesterol\n", "1 Vanessa 48 55\n", "2 Roberto 53 65\n", "3 Saulo 42 38\n", " Nome Idade Colesterol\n", "1 Vanessa 48 55\n", "2 Roberto 53 65\n", "3 Saulo 42 38\n", "4 Fabio 55 52\n" ] } ], "source": [ "#para selecionar as linhas, opera de maneira semelhante com os objetos python\n", "print(df[2:5]) #mostra da linha 2 até 5 (excetuando a 5)\n", "\n", "#para selecionar as colunas, passamos uma lista com os nomes das colunas desejadas\n", "print(df[['Nome', 'Idade', 'Anos fumante']])\n", "\n", "#para se selecionar linhas e colunas usando números inteiros se usa\n", "print(df.iloc[1:4, 0:3])\n", "\n", "#para selecionar linhas e colunas usando tanto inteiros como strings\n", "print(df.loc[1:4, :'Colesterol'])\n", "#repare que iloc exclui o último inteiro da seleção, loc não faz essa exclusão" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Nome Idade Anos fumante\n", "0 Jessica 40 13\n", "1 Vanessa 48 0\n", "2 Roberto 53 30\n", "3 Saulo 42 15\n", "4 Fabio 55 10\n", "5 Juliana 38 0\n", "6 Tatiana 29 0\n", "7 Jose 31 10\n", "8 Eliane 48 0\n", "9 Tomas 44 22\n", " Nome Idade Anos fumante Idade inicio fumar\n", "0 Jessica 40 13 27\n", "1 Vanessa 48 0 48\n", "2 Roberto 53 30 23\n", "3 Saulo 42 15 27\n", "4 Fabio 55 10 45\n", "5 Juliana 38 0 38\n", "6 Tatiana 29 0 29\n", "7 Jose 31 10 21\n", "8 Eliane 48 0 48\n", "9 Tomas 44 22 22\n" ] } ], "source": [ "#uma maneira de limpar o objeto e só trabalhar com os dados de interesse é redefini-lo\n", "df = df[['Nome', 'Idade', 'Anos fumante']]\n", "print(df)\n", "\n", "#também pode-se criar uma coluna ou renomea-las\n", "df['Idade inicio fumar'] = df['Idade'] - df['Anos fumante']\n", "print(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uma coisa interessante do Pandas é que seus objetos Series e DataFrame, tem métodos para visualizar os dados integrados com o Matplotlib " ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df['Anos fumante'].plot(kind='bar', color='b')\n", "plt.show()\n", "\n", "#repare que os eixos marcam números inteiros de 0 a 9, vamos colocar a variável 'nome' como index desse df\n", "#e vamos ordenar os valores em ordem crescente\n", "df2 = df.set_index('Nome')\n", "df2 = df2.sort_values(by='Anos fumante', ascending=True)\n", "df2['Anos fumante'].plot(kind='bar', title=\"ordenado com nomes como index\", color='b')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5) Scikit-learn ([página oficial](https://scikit-learn.org/), [referência rápida](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf))\n", "\n", "Scikit-learn é uma das melhores e mais populares bibliotecas de machine learning para Python. Além dos algoritmos eficientes, uma de suas vantagens é a interface homogênea para utilizar seus algoritmos. Essa interface é conhecida como API. Uma vez entendido seu funcionamente básico para um modelo, é fácil utiliza-la para qualquer outro modelo. \n", "\n", "### Representação dos dados\n", "\n", "A entrada de dados para os modelos do Scikit também é padronizada. É importante entender o seu formato, pois é basicamente o mesmo para todos os seus modelos.\n", "\n", "Os dados de entrada devem estar dispostos em uma matriz de duas dimensões, onde as linhas correspondem as amostras ou observações (samples) e as colunas aos atributos mensurados (features).\n", "\n", "![](feature_matrix.png)\n", "\n", "Os dados de entrada devem ser colocados nessa disposição, construindo a feature_matrix. Essa será a matriz de entrada para todos os algortimos, também é chamada por convençao de X.\n", "\n", "Outro dado de entrada utilizado em aprendizagem de máuqina é o vetor de rótulos ou valores observados (target_vector). Esse é o vetor o qual contém a informação para o modelo realizar o ajuste durante o procedimento de otimização. Normalmente é unidimensional e tem o mesmo tamanho do número de amostras da feature_matrix.\n", "\n", "\n", "### API do scikit-learn\n", "\n", "O básico da API é organizar as etapas de aprendizagem da seguinte forma:\n", "\n", " 1. Escolher o modelo utilizado\n", " 2. Escolher os parâmetros utilizados pelo modelo\n", " 3. Formatar os dados de entrada como feature matrix (x) e target vector (y)\n", " 4. Ajustar o modelo aos dados através do método .fit()\n", " 5. Realizar previões para novos dados através do método .predict()\n", " \n", "Vamos mostrar um exemplo de regressão linear feita pelo Scikit-learn" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "from dateutil.parser import parse\n", "\n", "######## processamento da entrada #################3\n", "dado_bruto = pd.read_csv('data/teste_temperatura.csv')\n", "\n", "#inserir um objeto datetime para criar ordem temporal, os dados estão amostrados de 6 em 6 horas\n", "eixo_temporal = pd.to_datetime(dado_bruto['date']) + pd.TimedeltaIndex(dado_bruto['hour'], unit='H')\n", "dados = dado_bruto.set_index(pd.DatetimeIndex(eixo_temporal))\n", "\n", "#removendo colunas desnecessárias e renomeando atributos\n", "dados.drop(columns=['date', 'hour'], inplace=True)\n", "dados.rename(columns={'dry_bulb_temperature':'temperatura'}, inplace=True)\n", "\n", "#reamostrando para média diária\n", "dado_diario = dados.resample('D').mean()\n", "dado_diario.plot()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# aplicacao da API scikit-learn para regressao linear\n", "\n", "# passo 1: importacao do modelo escolhido, no caso, LinearRegression\n", "from sklearn.linear_model import LinearRegression\n", "\n", "# passo 2: ajuste dos parâmetros do modelo, no caso, não é necessário\n", "model = LinearRegression()\n", "\n", "# passo 3: formatar os dados para entrada no modelo\n", "x = np.arange(len(dado_diario)) \n", "y = dado_diario.values\n", "\n", "# passo 4: realizar o ajuste do modelo nos dados\n", "model.fit(x[:, np.newaxis], y)\n", "\n", "# passo 5: realizar a previsão\n", "ajuste_linear = model.predict(x[:, np.newaxis])\n", "\n", "# visualização\n", "dado_diario['ajuste_linear'] = ajuste_linear\n", "dado_diario.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Referências de material:\n", " * Python Data Science Handbook, Jake VanderPlas\n", " * Quantitative Economics with Python, Thomas J. Sargent and John Stachurski" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }