{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "36acf19f",
   "metadata": {},
   "source": [
    "# Aula 08: Numpy/Scipy/Pandas/Matplotlib\n",
    "\n",
    "Python se tornou uma linguagem de programação amplamente usada em meios científicos. Em algumas áreas se tornou a lingua franca. Porque e como isso ocorreu.\n",
    "\n",
    "Tradicionalmente, a linguagem usada em computação científica é o FORTRAN, mais especificamente o FORTRAN 77. Também é usado o C/C++ e até hoje essa é a base de quase todas as ferramentas que se utiliza em computação científica. Essas são linguagens compiladas sem interatividade (não estritamente verdade) e o fluxo de trabalho era (ou é...) o seguinte:\n",
    "\n",
    " 1. Escreva um programa\n",
    " 2. Compile o programa\n",
    " 3. Escreva um arquivo com os dados de entrada\n",
    " 4. Execute o programa com os dados de saída\n",
    " 5. O programa escreve seus resultados em um arquivo de saída\n",
    " 6. Use um outro programa para fazer gráficos e analisar os dados\n",
    " 7. Volte para 1 até dar certo\n",
    "\n",
    "<!-- TEASER_END -->\n",
    "\n",
    "Estamos na última aula do curso de python e nunca fizemos nada parecido. Com python e seus ambientes interativos (tanto o terminal quanto o jupyter) fazem esta tarefa ser muito mais fácil, rápido e integrado.\n",
    "\n",
    "Mas aí temos alguns problemas:\n",
    "\n",
    " * Python, sozinho, não tem nenhuma das ferramentas necessárias:\n",
    "   - Matrizes\n",
    "   - Álgebra linear\n",
    "   - Equações diferenciais\n",
    "   - Plotar gráficos\n",
    "   - etc, etc, etc\n",
    " * Python é lento\n",
    "\n",
    "Este era o estado das coisas em 2000 (ou um pouco antes).\n",
    "\n",
    "Qualquer aplicação científica ou de ciência de dados precisa tratar de matrizes e álgebra linear. Isso é tão importante que existem bibliotecas básicas para isso com implementações padrão: \n",
    "  \n",
    " * [BLAS (Basic Linear Algebra Subroutines)](http://netlib.org/blas/index.html)\n",
    " * [LAPACK (Linear Algebra Package)](http://www.netlib.org/lapack/lug/)\n",
    "\n",
    "TODO mundo usa as bibliotecas acima. Se não usa deveria usar. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a15121e",
   "metadata": {},
   "source": [
    "## MATLAB e outros\n",
    "\n",
    "Um pessoal, por volta de 1980 criou um software que fazia uma interface interativa para essas bibliotecas. Esse software é o MATLAB - MATrix LABoratory. \n",
    "\n",
    "Mas o MATLAB, apesar de todo o sucesso, tem alguns problemas\n",
    "\n",
    " * É caro\n",
    " * É muito caro\n",
    " * Também é lento (para algumas coisas)\n",
    " * A linguagem de programação parece uma gambiarra\n",
    " * Existem limitações (legais) ao que se pode fazer\n",
    " \n",
    "Outras ferramentas semelhantes surgiram que competem com MATLAB ou focam em outras áreas\n",
    "\n",
    " * Mathematica\n",
    " * IDL\n",
    " * S-PLUS\n",
    " * e outras, muitas outras...\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8034f8e",
   "metadata": {},
   "source": [
    "## Python como uma linguagem para aplicações matemáticas\n",
    "\n",
    "No final dos anos 90, python estava se tornando uma linguagem de script popular por ser uma linguagem simples, amigável e fácil de aprender. Algumas pessoas acharam interessante usar python para aplicações matemáticas e numéricas. Com isso, começaram a criar extensões que permitissem isso. Após alguns anos e alguns traumas (numeric vs numarray vs numpy) chegou-se a algumas ferramentas básicas que permitiu todo o desenvolvimento posterior:\n",
    "\n",
    " * Numpy: operação com arrays uniformes multidimensionais\n",
    " * Scipy: algoritmos numéricos básicos\n",
    " * Matplotlib: gráficos com qualidade\n",
    " * Pandas: manipulação de dados e tabelas\n",
    "\n",
    "Porque python se tornou tão popular nessa área? Porque resolve problemas! Na minha opinião, python se tornou popular pelos seguintes motivos\n",
    "\n",
    " * Software livre: você usa como quiser e onde quiser e não precisa pagar preços exorbitantes\n",
    " * Base sólida (numpy+scipy+matplotlib+pandas)\n",
    " * Linguagem de programação de verdade (não uma gambiarra)\n",
    " * Linguagem de programação agradável\n",
    " * Extensível: dá para você interfacear com C/C++/Fortran sem grandes dificuldades\n",
    " * Interage bem com a Internet - importante para a ciência de dados\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "758dbbaa",
   "metadata": {},
   "source": [
    "## Numpy\n",
    "\n",
    "Numpy é uma biblioteca Python para trabalhar com matrizes multidimensionais.\n",
    "\n",
    "### Instalação\n",
    "\n",
    "Se você está usando a distribuição de Python Anaconda, numpy já vem. Abra um terminal, inicie o Python e execute o seguinte comando:\n",
    "\n",
    "```python\n",
    "import numpy\n",
    "```\n",
    "Se não deu erro, quer dizer o Numpy está instalado.\n",
    "\n",
    "Caso contrário, execute o comando a seguir num terminal (não do Python, o prompt ou powershell)\n",
    "\n",
    "```\n",
    "python -m pip install numpy\n",
    "```\n",
    "Isso vai baixar o numpy e instalar. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b074e347",
   "metadata": {},
   "source": [
    "### Executando o numpy\n",
    "\n",
    "Para usar o numpy, você primeiro precisa carregar a biblioteca. Para isso existem algumas opções:\n",
    "\n",
    " 1. `import numpy`\n",
    " 2. `import numpy as np`\n",
    " 3. `from numpy import *`\n",
    "\n",
    "A primeira opção tem um problema: tem que digitar `numpy.` na frente de tudo. A última opção você acessa diretamente a funcionalidade mas vai poluir o teu namespace. Eu prefiro a opção do meio e é o que vou utilizar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64ef9958",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a94cb9",
   "metadata": {},
   "source": [
    "O tipo básico do numpy é o `ndarray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fdc2494c",
   "metadata": {},
   "outputs": [],
   "source": [
    "?np.ndarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5471c0",
   "metadata": {},
   "source": [
    "O `ndarray` é usado para representar uma matriz multidimensional homogênea com ítens de tamanho fixo. \n",
    "\n",
    "### Criando objetos `ndarray`\n",
    "\n",
    "Dificilmente vamos usar o `ndarray` diretamente.\n",
    "\n",
    "A função `array` cria um `ndarray` a partir de listas tuplas e coisas parecidas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b494c147",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(range(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3db343ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af26da6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c39ba8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[10:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a15f608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "freelance-sydney",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  6,  9, 12])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dá para fatiar!\n",
    "y = x[3:15:3]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "improved-shoot",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(123, 123)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y faz referência à mesma memória que x\n",
    "y[0] = 123\n",
    "y[0], x[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "confident-block",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.strides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "terminal-gibraltar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.strides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "little-midnight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc63b2d",
   "metadata": {},
   "source": [
    "Usando a mesma sintaxe, é possível criar matrizes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3279d92d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = np.array([[1,2,3], [4,5,6]])  # Lista de listas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "382291de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c1bc959a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b503bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e0340b11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "12de9278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d92ae03e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d36a0a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4359f920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cffa70c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3b0ec89b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Array 3D\n",
    "arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b7f796c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2, 3],\n",
       "        [4, 5, 6]],\n",
       "\n",
       "       [[1, 2, 3],\n",
       "        [4, 5, 6]]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eab1819e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3320ba2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "49da4204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2, 3)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "effceeff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2, 3],\n",
       "        [4, 5, 6]],\n",
       "\n",
       "       [[1, 2, 3],\n",
       "        [4, 5, 6]]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e7b3919c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Indexando\n",
    "arr[0,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b493cb0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[1,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1e88e001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0,1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fb3357d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8a4d9a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8d64c360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 24, 8)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.strides"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "increasing-server",
   "metadata": {},
   "source": [
    "#### Melhor jeito para criar os `ndarray`s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "committed-belfast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros( (3,4) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "finished-queue",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, 0],\n",
       "        [0, 0, 0, 0],\n",
       "        [0, 0, 0, 0]]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros( (2,3,4), dtype='int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "nearby-chocolate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "suited-brunei",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 1, 1],\n",
       "        [1, 1, 1, 1],\n",
       "        [1, 1, 1, 1]],\n",
       "\n",
       "       [[1, 1, 1, 1],\n",
       "        [1, 1, 1, 1],\n",
       "        [1, 1, 1, 1]]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,3,4), dtype='int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "matched-number",
   "metadata": {},
   "source": [
    "#### Arrays igualmente espaçados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "equipped-employee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ,\n",
       "       0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21,\n",
       "       0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32,\n",
       "       0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43,\n",
       "       0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54,\n",
       "       0.55, 0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65,\n",
       "       0.66, 0.67, 0.68, 0.69, 0.7 , 0.71, 0.72, 0.73, 0.74, 0.75, 0.76,\n",
       "       0.77, 0.78, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87,\n",
       "       0.88, 0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98,\n",
       "       0.99])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0, 1, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "valid-payroll",
   "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. ])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,1,num=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ranging-belle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.00000000e+01, 7.94328235e+01, 6.30957344e+02, 5.01187234e+03,\n",
       "       3.98107171e+04, 3.16227766e+05, 2.51188643e+06, 1.99526231e+07,\n",
       "       1.58489319e+08, 1.25892541e+09, 1.00000000e+10])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(1,10, num=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "level-while",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.        ,   4.64158883,  21.5443469 , 100.        ])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.geomspace(1,100,num=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cosmetic-injection",
   "metadata": {},
   "source": [
    "### Os objetos `ndarray` têm *um monte* de métodos interessantes\n",
    "\n",
    "#### Soma de tudo quanto é tipo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "personalized-broadcasting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array(range(1,13))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "undefined-grade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = x.reshape(3,4)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "prospective-asian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "successful-blackberry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 18, 21, 24])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.sum(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "received-latvia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 26, 42])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "approved-mining",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1,  2,  3,  4],\n",
       "        [ 5,  6,  7,  8],\n",
       "        [ 9, 10, 11, 12]],\n",
       "\n",
       "       [[13, 14, 15, 16],\n",
       "        [17, 18, 19, 20],\n",
       "        [21, 22, 23, 24]]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = np.array(range(1,25)).reshape(2,3,4)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cosmetic-diary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "narrative-brush",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14, 16, 18, 20],\n",
       "       [22, 24, 26, 28],\n",
       "       [30, 32, 34, 36]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.sum(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "deadly-discrimination",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15, 18, 21, 24],\n",
       "       [51, 54, 57, 60]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "delayed-montreal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 26, 42],\n",
       "       [58, 74, 90]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.sum(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "confidential-skiing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([66, 72, 78, 84])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.sum((0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "level-shift",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 68, 100, 132])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.sum((0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "improving-broad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 78, 222])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.sum((1,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "complete-empty",
   "metadata": {},
   "source": [
    "#### Média e desvio padrão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "standard-hunter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.5"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "polar-inventory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.,  8.,  9., 10.],\n",
       "       [11., 12., 13., 14.],\n",
       "       [15., 16., 17., 18.]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "european-quantum",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.,  6.,  7.,  8.],\n",
       "       [17., 18., 19., 20.]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.mean(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "responsible-sharp",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.922186552431729"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "breathing-immunology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6., 6., 6., 6.],\n",
       "       [6., 6., 6., 6.],\n",
       "       [6., 6., 6., 6.]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.std(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sharing-coalition",
   "metadata": {},
   "source": [
    "#### Máximos e mínimos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "apparent-walker",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "victorian-saturn",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13, 14, 15, 16],\n",
       "       [17, 18, 19, 20],\n",
       "       [21, 22, 23, 24]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.max(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "valuable-array",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [13, 14, 15, 16]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.min(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "former-surprise",
   "metadata": {},
   "source": [
    "## Números aleatórios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "specified-moscow",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy.random as rnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "opened-plastic",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = rnd.randn(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "middle-research",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014018602934028006"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "catholic-cornell",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9904797535277062"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "refined-testimony",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "related-extent",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f051936ea60>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "caroline-sample",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0519254490>]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DecFqUR1h7pwohCQuqWMKIHpxUOlkbH0SrSbu37EptxfokzzVb66qVjNF8c2HNfQ8FDjSpXkFwmzn60pEKajzvGP/Ue9n1TFv2bIWX5y8kvHpOHDdS6Db0+Kci/o+tWupCIHPfuSyLkqrT8LVTJQBF++D8lwBOknrAzWHqUQg91hccJPOIcemJH/VcWzXyXX8LM+2/l3bc5RnzlMytqUz5K4bZQN1oUM66HD0ivXj2WBqG4eeB3Uc85xiaGyHLjIC6FgUQr4vANwXYbENgWsxM987q5WQ26CM9Bcnr3TeU2ohdGnD+xYRzuJKYcWSCn790xeNeNbFAAD53GSh3eXVBp+450nr/aKSqEorHOhexAHeIul7JmNSD4dGj9xVRGCb1I3mHO58+Bju2H+UvEE+HQlXBdq6nYcgAIyMCMw5WBzmMUJkPnWcPX8B0zN162SIRW0L2Vqbi5l6ELlGRrbH3UtDTuU7bGyM6ohAtSK6wlwKc1LiwJE6Nl++2nlPqWuhqhjNB9yVKPMdk4NzzXknPZaDEG/UlyfxOUKcOTk9U8db5y50vT4i6NzGfPv1LEwmc2yU0e7F3C5djHxq+3pUK51xrmpF4JYta8nvzEnZES/8/FfpVbbRnMPt7bgwV7pWAk4jbjuGT/KSwptnm6TuSqyycFdsT4ce09RzF8BFLjzQMvYuOuKgyM9SFMsVS0YXNHVsUPH/M293GxEObFo6RV8T3bBl+a1Vy6vOnV6I1ow5d2zgxLApBcd5SdNhXcqkPiaTggTwgX/7B125ialHjmHinid7khMqnSEH0O2KyFYlputhU2g053DmvH+V1R8uk3kQClsSx5aU5HKvqQciFrWNSkTdsmUtKQS05/HjVl7wWK2KZ3Zeg13Xb+yZHnVWKipl0E43mt6Fd7bR9KphuvKm5j0tmo6o0yVdCWCKVrl8yehCWMZ2rfOyZHRwk6DOugyJKNRaCmeb893dmOYl3jzb7InwXZTQihDidwB8EsAPpZQ/GeOYFPY8frxr1W3OS+w+eBy7b9iYK9ZnIgbzRQC46eru7VVodaEJ2+SLRW0L1TmZnqmT3stso7kQCjp88lRX8k6NT99a62EZXZPG9ru27+gIoT6GFkSFQK+Q5IQPsujeh2DL+1YtVGaurFW7wke1agW7b9joLGByhU9CCmdchtSloW87NnVtTzeauG/HpiBq7VitircvzHfcg+pIK9bu2YBbUWQFb5RkpxDiZwG8BeD3OIY8a7JzeqbuFPa5v02Zy5N9N6H8kTxHMZMhVHJp1fIqrvvgJVaNb98x9WPnZa2EIqTZs8kCEKIVLnLF1G0Ng0MTdJxkmStZzGXimDATwQB9vcZqVaxYOtpRH+HjbuvjdF0LleyrCIEt71uFb798ustAvWPZaFfTcFfyEbAX3yjjy028x2p+7EoQZyUJANmYTBR8rDPv94tMdkop/0gIsS7GsVzwece7Dx7H0V3XkqXEQHim/1LHhOXC9Dgo7+7Ns00cOFK3UtV0uLxsV3JFtfNSOxoVx1Pfo5BVgMj2vhqfeW9c96Q5J7s8mVAP2TVGc3FZVh3pMmiU1+aaT0pnxQRloPXwTH22gQNH6li1vGrd7ZhG31XwZRqxrXufsuYCli8ZxcwXru143bXLc3nrIbu6mDtJ167P9T3XWM0xhxTHmSgqZNYz1ooQ4jYAtwHA2rV0YtIFn8GYbTQxcc+THQ+h4vf6JGqXV0fQnJdd20sq5DECAKKVSFEUNGrbbN4813moCjqBFkXszPk5dqjBh90H6bCUy/j72Ac+Bo5t8uY1xFkTuL6q4NlGE7VqpYsSaTM2aptNbZ4oNtTTL7zBGnOjOYeloyOojghrlx/bPbM5LiYzJiR+bTNy2zascbJq9Ephk9aps8dsGi9Zqzn1796yZW2XyJdP+ZM7VqA1F8zOSxwUUQik0DNDLqV8AMADQCu0kuUYHMqe2TbK1vxh8+Wru+RKzzbnUR0RWLW82uWNKWQppLH1JOSchwRw5vwcKgat8VwOeVgqGedK0nEoja54bmizZwo2zRKuV6TH4c1FiStqZBobVRwyz7wd+jFDzn220exiaVFZU1P0SncAssav1XF1I6dLR5ioVkTX/eYKdmVxTmzHPnCk3hXC4dIhQ8TFuO0eFW7ZsrYwKmKpWCuhlD0qWTk5MY4VS7vXMLW9/MHe6/DMzmu6HmTVosp8T/+MzkQZq1WBdgxYpyS9eeZt9jmYtEZfAja2eBjHe9PPGwCr/Dxki2kzDty5oI+BUtyzgfJOXW3KfFAJQqpM3IaK6Oawq1CTD9QCRV07VaPgwp7Hj7vP2/JWCBUxFNxjx/7c5MQ4ju66lsWUEwBuLbiCuVQFQabHwUEotzpvFx49Bmzrpt2cl8FbMu4Yfd4EFW9V36UMLsd7C/WouKwMvQLR/D3APReqFbEQBghNUI0IQV4TwD1PqLi5BILG4Upg2n6fkwDW49emR6lqFAA6Z+LjVjfnu/MZnGctaxKe+xzH/pzvdcCe6C4KUTxyIcRDAP4EwHohxKtCiF+JcVwblFd0/45NLI9MNzi6t5pHPMcm3KVzRNX7eRkzFKgx+ryJXddvJI9JeUd5xY2oHYLpxVM+qmvNU3Phpb3XWTn4zTmJB599eeE+UbD99pyUTt4vdQ8qolWcRs3NkBmhXx8TSlZXv6acvIMev7btSmN4yrYqZtdYOM8Ttcvk1k7E+pw5FkqMbHys1rF7L1JmG4hkyKWUn5VSXiKlrEop3yul/HKM47pgC2OYsUSq8lCCbsUVowtPXs6xjsoIfU4mfF6DyyugvmsrXOJqt9w9/Tzu2H+UfECVIR4fq5EGjmtYZglPkWM4a9URq8qh67dtRWLVkZZuxxcnr3QaYQ7Gx2qYnLArVgLd1crTM3XvLtWcO675QhmesZq/aI1TxayPxVZMpq69bw5NbV+PqnHzRoAO9dHpmTrbIXGpma7beahrLG+du+C0O4B/oYqBUsXITegxy6O7rsW+z1xFGhyXoLxaCJZVRxZkW10X2Wcw81AV9XHdumUtvnQzfU4mOF4HZVyoDidb9z61QDO7b8cmMj9g+64rkagjhL5IIQ+t62xznvT8qRCGrc2dnvdUc5MTDXdJH5gLqa3SUkkEuH7LNneoa7ayVsXUI93l5tMzdey+YWOX4aTGrmBzutSztmmPXeQK8CejAeDwyVNdocp5oKuiEuiWkrY9S648F9DtGOgSDrbjqhBr0TLbpYqRUzDjazp1zCfGMy8l7tuxKUjgxxU3dgnbCwCjDgEmqmCCG1/j8HG5nN1Q0SMTId1RstAXzbHadE7yqAO6fpvS9JizxIfHHHkJgO6JaSba1d9X7DxkPY6LPaGSxeZ9o+bC+QtzVpqqKsZT1Niz5+cWirooppd5Dua88rE+XHNIOQs+KOE81TtV2QflqFB5LqDFufeN8XSjiaO7ru163RdijamnUzpD7uMB6wYH8Je8u0RzTNVEF91O78JDGfH7jMpT7kPguwb695aOXlRss3HOuZzdPEqKvm2+besdSl/Uf8tVJesqrPIhC3VSKWJSsqk6QsrPFXwLgw16QZU5d2yLiKt6GrhIjQWAFUtHg84hVthRPbfchdrk9B8+eYrVxSrPbtB3rjGLg0qlR+4S2jfhKiFWCCm/Nr3lu6ef7yqlH/d4li/lKM1VoMqJVcchk8NerQicbXPPQ4qJKG1tTmML1+JJ6Y9T91aJ8FOLl6vc/eiua4MaJtiqJc2dXQzpBzW2LNi058kg7rKOW7estVY9mqXooWcWq/FDCFwMLA6EAFnINc6YXwquc/c1uQjtE9Aad8kbS3CE9nX4ttV6Mwlf8wAF9aC7RJo4DQTy6J1k7SykYNMtCfkdn/6Fa3yu7iiu3wvRHNFhdodyPViu62JbZPIgj95GLEOowyYOFQqO9s/KWhU/OtfMJDjVa+gLHCXz4dtRUXNaNbPIQkmkDHlpkp0h2yiglbChUjICnVstW+bZhtlGs0Nv2wZdh1tB36LfPf08bjcy31OPHvNmsPWmDXnALSbJSjt0bUXv27GJLIpwNe/IYsSBbkqlayurCq9MtobLiPPLejqRZ0tdhFbHbKOZO9xRb8esdZhsjdlGOYw40BlGvPfGKzvYOmPLq7ifkfinnqGsRtyF0hjy0MRAc27eaWw7PmtknikNZi7Uam1msadn6laj1JyT2PP4cfJ4HOH9EHCuJYd2aKOoUYZG0ekouHjZWWGe59T29U6++u6Dx62NvV3zKEtzEE4FpQ79Op95m3Y68s3a/DApdSHx8Fq1gq3vXx31HGrVCm51cPp90OfP2xcucpJczV105KHuhqI0yc7Q1mic5hE6ZhtNnG40ncJaXFDbzN0HaWPtCu3E5KUDrd2KrkVtJlzVb9pYQAoUq8UWq+d48lQCOc9526pPXYk8W+zZWUgkkGl8nApKBRvLQ2kCvXm22REizDJvBXgJ1Fp1BOcvSGduwKyl4D6vKkQRuus2QUnxbr58dcd8fvPM2wt5IxdWtr3wPIn/rBoyoShVjDyG0D6HTaCShxxdcOq7NjrZOoI6pjA+VrPGzX3fC8GIaBUZkaJHIwIQ8HYKd8X/lO61q6G1DbbcQR7JUFtyN0Z4igNODsaVXPWN1xWTtok52e6rylkAcO48qiMC+26+amFX6RLNAsIWYP08YsT/ORrmVINmEypvQuXmOIn/2Nr/pW++nEVnxUS1IrDjp9xa30BrtX36hTesWtI2uLwikw7pgjovkwrlWnxcSV0V11MPtSpjd01iGz/a5n1Q4Rk1zjkpFzxx7uSlvBcfHY6C8nwPnzy1sLCurFUxItyl/3lhGhMX91vXH79j/1Hcvv9ox+IXqvGhrqHOqqoIgR0fvqzLM9Ulmp2XQ4t37HvihNOIh+5S9GbiWRuS6+CEDakqYBMqnxSqFgnkr8MIRWli5EC4zooJNQH1uBUFNdl9uHXLWrx47yfw0t7rMLV9PR567hWSkx4CfZvq2hW4WBi7b9iIo7uuxUt7r8NLe6/DzBeuZU9iE1z9DB0xqteU2BcFmzSDOYavaHors41WOGJ5Nf/UH6tVWQlhSo/DhCk568o5rHSUyk/P1HHgSL1jUT1wpBXPNRU8OWE7PUHuM5Qu32esVu267nq8OWtDch2ceRmSMH5ttpEp8e+qTSlCb6VUhlzBFFwKgaoEUxPaVbLO0V05cKSO6Zm6t4orC+9YPTSh57lqeZWk0mVlPdiKeDiJKdXUIM/k3XU9XRbenGs1vQ1JijbnJVatWJpLC0X1tPQls+6efj4T31mXnLWd+xlHwjRENpZLIlCfyzp/xsdqOLrrWqxasbTrPZMhoq5naJ67Vq1g24Y13vlmM8yuxuBZEv/UzsKmkRMDpTTkQKfy3f07Nnk9bAUJdKyK2zasIVdbnzcIXNS5sOkp+OAzJOqhCfFUatWKs+jHdyxVRGQeUxcfU/ornKVJdajJM3knJ8bxjmX2KKBKas9JGcR4oDwtF2y6PMrY2nTqXSXkHCOlJGdt5+6ikYaEY7iGOctcVNDnD0fY7Zmd1+C+HZsw6rhIQrR2w7pxVYl233yzGeafef9q6+8ocTRd18l2n022E2cuxtRbKa0h16FfZI6Xpa+KB47UcdPV4x080bcvzOH2tnjWdR+8xDtxZxvNYI9bAAvKfxT0+CF3B+KbHDZerHL4xsdq2HfzVaT4mDlhOefoE83iyntyQkIhd0D3tDiKfgCwrDoCiZa2hinKZBu3i4UhpZ+6qIwnde6mnncWiWaOYaZEvCiM1aqk98qVk6X0bBRGRwQ2X756oePWa7MNPPjcy+ydiGmYX/ob+wLDactHNSzhGPNYeiulSXZyMD1Tx6mA7jtA60Z//djrHTxRNX90Q0/148wK3cOh2DgmTc0mOmQDZ3KY56ukOqk+hQCPBqmM91itSpaSq/GFJIRiJML0MeqGydQgAVG+rbx/2+J0u+adq7G77oNKaN7x8FHrb+ljpM5dNb8AOqsPQySa1VhtTVCA1i7EDCPoCVVbyT/VTxTgi7b55nBzrtVrtqMiNUDBkvuZPN9V9SQuWYdYBV5DY8inZ+qZGqICbgU2xWB5Zuc10SiQQOeW7fDJUyTV0WSMcNg7ZjMNG6XPPIfmvOzqd6r/HsDvhgLAydRRPHbb+M3z9alXhkJR7sytsX6Nsv6Wed2oYykjffjkKTI5KHHx2lOLvWp+saw6Yp2TFSEWFP8o3Rj1HsXQmpfSapRVQtXUxrnpaj9v2ibsBqBDiZDDbedqzpjGUp9TPikP2R6Xi31F3WdTloOzgGVFaXjkPrgSDGpCZxU70vmi5kPA1WkxoW4yZ3Gg+KqUR2SGQrIuPCbPmTpXk27n42qbDaVNqPMNHT9HttZsrg34FTJD4bq3aiHZfPlqZ57BvKZU60AXbPPWZrxq1QqWVUdY91bBdY/HtWurc9qXV0darQ41+qIp7FYE7jdkrbPcb1sthW9BMOsYYvDKS88j98HlLc5LSRqGWrWCEeGuBNVXdJPrnHViqPHauqO4fl/9pq3ow/SI8laEmjxnoNsIZ9kWu4w44K6oo8Ap9BJA166D8mbzQE/c6butihD47Ecuwxcnr8TWvU85Fx0lh6sbgzsC+fRq3pgLvi00RC2Btk5IgF/Kd+qR1qKj32qbsc7Tw5ZTDzBWq3Y8r1mfCXOnePf08x1yG7Zh2EKjRVV5DkWyE3DHmvQ+hWa2+qarx3H+gtsbqM828P67voG7p7vDBaEJM31M0zN1ljeva3Oo1ldUObmenIkpXK8wNy8XmDwVIRYmOKePIheKWscdvxIict0DKvGaRwqVgt7f0cbnDjm3N882F0TVQq6rWmCpTk0mGoRHrM8nTkJVoTkvCxfImvckjAWAT151ScdreZ4JPbfDFXIrkjuuo9SG3BQTslGNVSd19bndB4/j7PmL3WQOfed1lkcwJyW+8uzLuOW//0nXe5MT9ka2FPQmFByolZ0jp6pP1CKU8oCLjAtTrF/vo5insEOvqLOBYkVQtiVLp6CsWl36DsXF5w65N+p6hFxXdU3y6peYiWlXz9teQ917NRdqRrGRRKtuZB1D1E0Hde/Vd0Mpg0Vxx3WU1pDbJDLNir1Vy6sLJfn653TqWKhH9syLp6w3whUTXl4dwarl1S7DE+IdNJpzeOi5V7wPpT5RsxpU1cqLgk32VKd55SnYUnBV1O2+YaOV0+tqwEzVA9iqMwXcFYou6HFUlzSvrTWdC4pT7uvfCXQqTebdlenGi0qoFo1adYSUhtZphKstxUZ6taxqnOwbsQCctRR5rmnsXp0KpY2RU8yLH3/nMnzPSLzFjoGauiOuPp0AICG6inSmZ+rByVeO2Jcer86qTyMBVCsjEJgL8uZUFaeezPk3j30nUyLrUs0YcRNELsaJzcirReHwyVNdMeQsXrwp1etiXpihMZ8OzogQuGLnoY5rwGFC5GHhcIzXnJQYq1Vx5vwFpwaLCYFWLsT3PVsHI2oeeOUDtP+77u+8BN65ZJQUNMtLhS0i5BnFkAshPg7gNwFUAPy2lHJvjOO64PJ2uD0TKdSqFbx9YY7dWd23fbVR6lzl/FlAtXHTEyxmktTVLut0o4lbLK3BXA+AquIELm4jqdirC3p8NyTL7+o3aY55xZIKfv3TdPgh9M7Ykr4ht3f5klbvS4pCa4axAF7/VRt10bdI6VRSjvFS0rpc6K3ubD14qUbUvkRhiIHVOd62a0E1VAbgvE8cFBHyzG3IhRAVAP8FwD8C8CqAbwkhDkopv5f32C74eKZZjeTy6giWepgMJrc0tGggtr74rUT7NBO2rDlFI7t0rIYvTl7ZpZjnelDsbIhwLKuOsJvjmosT15M+q7GUsnhIY4zG2acDemuqrf/KWhXnL8wt7GKoJK0SYfMxIShjb2M9qfPafcNG7HviRIcEgatwDWjthrnOkx6NMR0N7sKtU//U7yoBNe7OwDWnXcZWjcmkVnJ2njG54zpy88iFED8NYLeUcnv777sAQEp5L/WdvDzyPMU/MaE4wZyqz9i6yzryNHWmtudUJ5Ne6XlTBlnntuftAanuSeg5cTSvgezXSg8nuCR8szbwBezPUHWkJXdrawxy741XdoWgqDFxqn/NuoiQeehK+o+0f4AzJxTfPWT+U+DUscTQJC+SRz4O4BXt71cBfMQygNsA3AYAa9euzfWDPh0GCioux60IU6A8DZUVv2XLWqfGecy4pYk8CUXl1TSac9ZGEDYPiZr4VEGJC2YTCh3U3dW57a77yCkCUzF9ToWfDp3JYfMg81ajchNi3C41NlCeuk850XWN9E4/oWXpLtlXXTbi8MlTTurfvG+QGnSjaoZ31I6Ea3xdu7oienSaiOGR3wxgu5TyV9t//wKAD0sp/yX1nbweeRaPNsR7tn0XoOeHOYFt7dNiFBHZYAurcLaoPg+Iqkq0JYpWLa/iug9ekqk9XshWOAR6dairw0uWX3Z5ch9auxL/98VTuXdcvjmnf87VpSYU1LPlGw9V+cj1djnPdHVERNuF63F6Hdwxcyu8qd/JiiI98lcBXKb9/V4Ar0U4LgmXhgXNHHG3s/L9HkBTDOuzjS7BJBv0m7+yVo1iyE11Nq4Qla8PIaXopv9f4c2zzQVxsdCO9805WUjHHr0IzBYSyGrEgVaBlq0it9GcwzMvnsp41E745pwCRwsE4MeffTFjqr2fzTiHsI44u9RYRlyxlRT0a2PbRTSac7hj/1Hsefw4Zs82u9g29dnGgtSA2SJR/U4Rbd90xPDIRwH8GYCPAqgD+BaAfyKlJDsNx4iR21bNIlQKASxw0znJDIo9EuqF68k0V3jA9MhcPR71XYPrrr+097pMu55xpvGxIW+jZR2q12IsYSxuIisWsuiPuOK6Ls0X226O8kiBbl0aquGxDS5jFnOX6kMM7RUbqB6soXkoFwrzyKWUF4QQ/wLAE2jRD3/HZcRjwLfSv/+ubwSxVm71xLhDHiiqQzqXqaL6iuoUrG0b1pC7CTPe6KJlciasKvDIEsd/bbaB+3ZsyvRg3HvjlaxFxgcODRMIS0RmoVBmxfLqCM5dmEez2X0VWoyqinULrxqc2HIbZ89fsO6uHnz2ZWy+fHWXRC3g9qL1XeUZLaTgkiH27RTN380icMfhs5tcf47WERcUZdG3+42BKDxyKeU3AHwjxrG4cNGuQifA0y+80WFIsqokKoQ0KzYxOiK6aHcHjtTxM+9f3RV7VRrieqEIZYCVLooP6rxdiwcFs4gndCFQTJAsbA9OHFPnKK8MoKrF2NAr1UVqblWEwJd+7irndVu1Yime2XkNuVuabTRx9/TzXfOHgkR3cRtgpwTqiT/9PpkJZ8pAcYyZ+bucptvmzsKVaDZJB1ytIy4oymIerXMuSluiT0FVWYZAlT+rUt8YhTpZmhUDLe/PNuFf+puWt6vKs8dqVUCgq1MN1bqOe05jtapVa9qHakV0FfH4GiPr0FkaWaQFbJ2HzPZbZhPmvBZaab64INDa8c184Vr8YO91Xt1v18PN6Zv5lWe7u+S44Po92zXUtUJcuz/u71CvT07QbRZVy73xsRru27GpIzyknmOzBaStz2bsUvmzRB9Vqvl2zMKgoTPkex4/Hvx8mo0YOKbHptFhHnN6po5Ne57Eup2HcsftzcVmxdLRLm9S6bHcdPV4l8IjVxPjDJHI82HFktbmztS/4d4M/YE2NUXGatUOrRrOMVjd4efDmjbr0DVfXEeQaO341APua3XGUfGMWVDi+j0fFZH6rgC6DBq3xZvC9EydrIx9Z20U9+3Y1KGzQ7UMPHXm7YVF6HMPH+1QMI1dKq/Cqvq5T8/U8da5bl0d5fjEwlAZ8ixbJXO7xVGL0zuo26RTVTfvqUeOkVznEG8V4MfClVSqagg8tX099v+pvfuQDc05mWm7ebrRJPVvOLbSPD+1aN23YxNWLO1MplHGXD8G9yGdkzLY++f2oVTQPVlKCEzNwant68lyd72Hq68puAmX8BQFnxc9tX29dRFTIRsdtvPWQ4O68VU7AerZ0dVAt+59Cut2HloQxFJGe+qRY7h9/9GO/Ma8bO1YlDEvolTe3BlSNS8rloxGZa0MjSFXHVRCoLzVfU+cWJhMnApN/SFW8rXKs1PvP/3CGyRdanyshqO7ru1ocuyDuW1zTUKV+Nq69yncvv9oTypgVQNcG6TsVpPTofen1EFt7anwkVl0xYEQWCiICsEd7ebc0zN1bNuwxnsP9XiwqYmvz6fJiXHsu9mura57fLuu38hegFr5gw92heaWVUc6zsOEz4uenBgnnR5zLth2WWZocOqRY5i458m2AXbvphrNOTzYDpUB3Rs/15x/6LlW/aJr0cyD+mzDG34KkW/goDSG3NVtXTVbCI1tr3tXrUPi1idxqUtn6kYGuOjZcfRX9A4yKlTii7Wa2zbKG1KYbTQzh3N8YSMT6rypB398rIZ9n7mK/D5116itvUpOu+Kf3Di7mjJzUrIWVCUMphuf/d/yywsD9vuuhwcUJifGsfuGjdbFhVoQqIVI53hPToxjavv6hepmM79iGnOOF03tDGxz4fDJU/ir0+cWwm5maFDvG8tBVvdkTkps3fsUAJCLpkJ1pFMamwt1PUNDSllRCkPuSrpwO6DY8MyLp8iiFxv0bVPW+CH1HsfwmJrfRfjZetjI5aXqCSdlKFxhA1844I79R7s6MLm29hxjGKqJrqpWKdiKiMwelC5wH16fOqZtQfjSz11lvfZ6ebgrZKF2cTo4XvRb5y44tbsVVGu0QWhIAXTSH4/uuhYvEc5Uc17i7QvhY1bX0xdKi4VSGHKX0fTFtPN0qrFBPUSujP3WvU9h24Y11m0bleQwHxoK+rYtj87KiIA1kah7bxTDArjYB1U3oqbx1FvB3T39vDXpo6B4zZzwEdcgKkN3/45N7I4/EiCTrHlMUMjD60vU2s7fF7LhHHe20ezyyn0J9ua8xIolo87fBS6GM2LBdzs5IRNz8XLlnSiYXYl0qAXTd19ioBSNJfLwMGNXiunsAlfJ/oEjdXz4ilUd3G9dA9sGnUfritcrT8InLUrBVvlog+scl1VHFsZoCm6Z46rPNli7JpPXTOmZhHgzygvlOoIuZcPQQjObEJlrnJyCKP38bZWSLlVGzvPiKlJxxXt9eiIxPHGlH65qAswiPrVjUtcbAO567DvOgi61eE1OjAcXwY3Vqnjb0+9XceEVVbIo8axSGPIs+g9cmF3hXdAbHvhadTWac10FPNyco+/4ystVD+2dDx9jdZDXJzlnQrkE9BvN+Y78AODuTM99jPV7qXRS9C70N10d1omc8kIFgFGLNoZrkeAaI6o4yeyeFFomrse7bZWSd+w/itv3HyXvMcdQuYx9Fu1ufex5jLltgTW18m3idKtXLPWes1q8QhyjWrWykCjnwFX1GgOlMOQ+zyyPVoLqCk8lWUwt4ZDfM6ctpyyX+1DrcVLbmEI0MNTvUg/FHQ8fZXu0jeZcrl2Q4iCr6jw9Lj0nJR589mV85dmXWY0dpmfqzod432euChIyGmcYQpsRpcrTD588xdYGMhcHl6gZZTQ4hspllPPskD77kcuCBdVCf0MHp1G5gvkscXZGWea52RAkJkphyEP0H7JohCxfMopd12/E1KPHumKA76yNdmh35O0BqmLc1I3karLoD5xZFl8RLblPdV6UkJKpmaGruemG4A5GqTQHHMVBCeDOR44t7JLMz6u/9YSdzXAp4+n6HY5ipQ6fIRSANbRB5Xi4hk3tRAAsePW+62hzGsx5Yt4Pm8E0F3glTBeq4qeqL9XuygVTfMrUB9+2YQ2+fux1cg4AYUqnrmcpNuakLMQzz61+mAVZ1Q+5UpAT9zwZRGMSAO7bsYkMI+jeEFcV0GW0XMpnnONT3/cp11HtvShwmjPYoGKH5g4hVNEvFPr2O0SvhRLaAux6LZQxouLrMTpCVdvZ6dAGxy6tctvzBHSLYpnhpxjJunU7D5Hv5VEnDFXgVPkigF7gigC3y5SJIvXIewLf9lR/0GzsCJfm9aVjNWfXId27oTx+mxdBKSq6Qiwu0StfuyjK89t98Dh+1Ggi1IQqYxVixAWAT151SUf8UhkE3YiPAMHj8UGP74aUX1OKlbY5d+BIHZ/9iL0dGuXNxjAKWYq6VtaqXXF5oLsbjnp+dh883mG4KZqiOXd9C4JtzlJhqrFaNYhpY8JXC9IFia7+sL1wbWPLA5TGI6c8LHP1dPV6/ORVl3RtuZSHQXWR0Y+rOs5wtYV9Cm62buVZtYu5anF5wG6ui05FOvLeibBO8z5k9cht33cdw9R2L7oLFAfmvLd58Fm8euq3lKdvO0/b75hzmDvPQ3czAv7G7Ca489qVSwtFbI+8FDxygF7BqBiqidONJr44eWVHmbLO6fRl3iWwUA2mi1C5mBSTE+NOrrcqqrh9/1FM3PPkQuw8lHfqiwfHwryUuH/HJi833+SEu0r3Q3j+vmId3SO2FWL4PDVVA+CTa7AVJAFY+O6mPU/icw/7y8xN1KqVBcU+LsbHanhp73Vd8/ody+yc7xht9fRnhdLXsQm63fnwsYVrC/D41aEVkBLh84q743zr3AWEVvSrkKKOIgqCShNayduwWNeHsBnFqe3rvV65KsmG6Aw7HDhS7xLoV9i2YQ0rqWVu72NQ7EKgWC4uj8PUG3cl3XROOHXvuM16gYsNN6hrKdEZFjHHubJWxfkLc84YvSq/B9wxVtO4mN5lSB5CwWS7cLx5ffEy58wVjhh0XmzbsGbh3yEhApOmeu+NV3q90iy1EqcbTdy3YxOLlhuC5rxcKOfn3mP1HVvnoJgojSG33VAqjMLJxgPdsT1b8wYTdk61PeatdL258NETp2fqHQlLlaTLG2/TCyhc4Rmb0fB5rup7FG1NP5bL+DTnJJ5+4Q0ytjo+ViMLZC4aWrcR5zzytrkUspDqv+PqZqSO61ssqbmS1/FxQe8Tm/V3Gs053L7/qJc5ZGOsqbg+9bvK4YjFttJxutFcCCtxw3ezjSZWLB1NBUEAfUNtSScORcqWyDp15jxu2bJ24bsha7nNmGbxlCmjPD1T72LVvHm2ialHj2H5kgrOnA/3yG81Oqu4wjNmEkrBtZPRd0GAvwmvzyjUZxtO2WCqlZjvPnD44epztnFzF9IQxgdnsXSFYLg7wSzQzzdrdbECp1CG2qFScXa10BaxmGWRSgZSQVAH1A1VnteDz76MlW05Tm7hi4JLWU9t90JojLZYXhZPmYoJUqya5pxEcy78IRqrVTs6q7iMndl1XIeqvrQlkXXPlRMu4vC0zS2t8mpdejyu+6CSTj7viuKIAzyDQXWa5yBLIY7uNYdChbEomqWLd23b2bgYY0D2/pU+ByHLIqMICBSxwpRKDlkoYvfp1FGaZKeCqYQ422jiXHO+q2OIDz79FqqzBwXbQxWaqHE9nDHpSjbD7Dq+zwCZSWSO1rUNKtFr87qp0MfytkC/63667oMe/vF1fKLg+66pQsiBLtu874kTXV2ffPeEO1/GatWO671qeRX7PnMVvjh5JamoaM5RlfilhMXeucwvi5x1fptJZzNP4ppPNqysVa3yGIqJZVbKhuqZx6YdKpTKIwfidaT26Ua4eOUmbGEHjh4LwOOHu8YbCiou60pIhoQCfN3SbTBj22qR4VTr6n0sqfsZEv6xFU35vF9bYtUnH+ACxV/XC7tub+uqhN5P87x232AvhLKdl+9cKCM122ji/h2bnNWSefW5qWJB9d/d0893aPZsed8qfPvl0120SbMACvDnMmx5K+pci+hKBJTQkMfqSG3rEq+LYnGNps275XKIuSqEQGv1z8ITH2lztX0PYQylQSB8obUZLZuBokIfeh9LV0KVCv9s27Cmo2jGtohwDHEo08gFV2HXmbcvdOVJ7nzkmLUwzswfhervhJ6Xa/E4fPKUkXjON890+JwHRTrQmWbffvl0Vy7t7PkL1lDqck9bthVLR3G60X1NY5+nC7kKgoQQNwPYDeDvAfiwlJJV5ZO1RB9wJ364BHvbZFJbp82Xr2bH1Uxjo4s9cTBWq1rlPynvwlXW7MJLljLtLNV4HFAFHFS5uCs2rScHOQUkPgkHW7m9LVlOhS24EhF5EaukX5dEcMkQZAE1f1wUXlVQw2HuhMBnE7g2w3XddQlds0sYNX+KmC9Fleh/F8CNAP5bzuOwEcNztHWJl2gliJ5+4Q1nso2Sgs1SyWfr2+fyLrjsCh+o3+Dwen0IlTp17aR0T57a5gMg5WEBt/GduOdJ9u4hS8goK2KE0ZrzssNzP6dRL/MaGNu1mHrkGN6xbNS5AClvV//MuQjaO75dOncX70py6vUFLjaUOWeLohuayGXIpZTfBwAR2Lg2D0Ljdgocb9mn0+DigWahGtqMm2tyZMnC2xI9sfIMNnAWWt2Q+Ajc+sNmPhicLTX1PgCSkcSlkhbFQqCu4bLqSOYScTVWALkXJKqaM8vYYlxDn/PAdS64tSo+NlRRCU0XehYjF0LcBuA2AFi7dm2uY4WudFxv2aXT4Ev6hd48ahfh61MJ2BNyIwBg0LyqI8JKGyx6Ai4dvdhYYkR0GhHAiB16Ygiu5JDPuPr6qob8Zq8eWrXINZpzXR2GAHfBlg+vzTaiLEixzzn0eOaOYt27al11H/rzxd3F25xEV5I9T6ON2PAaciHEHwJ4j+Wtz0spv8b9ISnlAwAeAFoxcvYIPeBsE7nesq7TEBq6cd308bFOlTnXLoI6zogQHTrmpnZ6pc39zfMbMZgD5gOjFhZX9yAKvusea0ttgqKSFv3QmtdvTsqOhC3QShqahT4jaN1/n47Kpe04rw0hxjR2oU3INbTtssyxCKBD/4gqJtQ1zk2Wi4IryR6LIBADXkMupfxYLwaSBdy4ZcgkVToNoaEb6qaGFoFQ4RNdkH7fEye6BZHaJeycGLdrAuaJn/oWzEaT31VFdQC6wyjj1sdHabNwt9QcGVWFGA+t69pOz9St2iCmt7z58tUdTRVUshDw64grXZu8CxI3xMdRFQy9hhynTOW7dOgGOiTf4WNDqTEVnQD3oVT0Q/NBOHv+AmubGOJBKJ2G2FVmocdxPdR5vSpX4jBP/DTWlttsTKFrz+ssE5uRUImprXufIlkptnNV77kqWAH//aWMtS9ef9djz5NGT81d245HJQtt+QNqrHkXJBtvnmpA4WKxuPrHUuPnzjHX50LCS7773suEpgt56YefBvCfAawBMAvgqJRyu+97WeiHIawQk+rG/W6s7icx4KNC5aVg2pBHf9v1fR227kE6XEk9ysNTr9vE0ly6O7HpYS46GuUJczraqA5WrmOE3PdY5222C7QVQWWhC2e5jiHHD6XIDhIKoR9KKb8K4Kt5jsFFCCvE3Ca6PNC8E7oobrGLCuXzNLOC8mKU9+jz1H1bbt3jtWlzqDABpVpHeazz7YSgeb1M7RwTsb0pytNzJSg5HqaEfYcWcgwdMc7bJt1bq1a6mF1ZQlI+5hbVkpF7/EFKUsZCaUIr3MlarQjrTaQmr7kddXGSTZiKhAt65cjPLabKylX8T3knMReQlbWqVWdZtJknOmxbUduW21Wqbj7gb527gD2PHyd3IpRHHiuJxwW1eGdJALri9TpsOw7zGHkQ6pBwwxNZQo4+5taex4+TVEdXuEYha75DpzCbjKJ+7+JLY8i5ce65DL0NgWwFH7sPHu/yDJrzErsPHs99YycnxklPTk3o2JOnOWcvzqCib7YHjjuuLFxkmz5GliRenl2Uq3dsaNNeNXYbE8UGCTuv+fXTDazbeSizUcky913GltKF54J61lUPUmqOuBQqdWRZXGyMIqB4eVouSqN+6FOYU5iX8PKEbcjCObZ5r67XQ0HpTRexBZyeqQdrmucZRxZP+aW/aZDtwWzzg2qIrKtnqgeRq9BIzZOHnnuFbcTNsYdIzkp0F3mZFE/uuShkmfvUvV9Zq3Zd36lHjmHinicX2rz5xme7l0rQKqRzkwuTE7Rqog2u0C6nNqFolMYj11dRn2eexUgMUpWWQi95qq6JaEtQ5h1HFi6yayfC9bLyFsRQ84HbUsyWhAuZY2pxp5wF81w4u48sc5+am7YwnL7T4jaSADrvJSVopf92kfxt3z3qp50ASmTIAbfUqI4snmKWBAjVVXvV8u6y+CzoJU/VNRGzKgK6kEVuwLwXWbbwroQuJz9CzZM8nGnuoqa+72thps6RGzLJMvepuclpr8ZZOM0F29UGsOg49fRM3dlPFuh/orQ0oRXg4sR0GXEq2ekDd2uuY9f1G7s6ZFcrYqFAIwZCt4AU9EYFtu0tNRGFaFUTxl5MJifGO8IkY7Vq17XUYdNryRIiIc+zfQzfsah58tmPXGYNB6xaXvU2g+CEDfXv+4yGep8bMvHNfWru2OYm16CFerDUcdUOp0gj7uL4A/2r5tRRKo/cR0HMI4mZxfvtlcdchFqd6ZlRHrKU6EjEhSZ3fFxj3YPm8JIVsoZIQoWRQgpENl++OtN9chWBAd3hGNduRjcq3JCJ65w4c0e/b8uqPN8w1IPtRZjR9pwVaXNiolSGnJqYsYj8WSs6i7yJMeRTfUZPF2rigBtTtnGNFczKRv0B4nQbz5rTsBktX/ch2zGoOH3WuTA5wet9ap6DiwrnC5lk1Sky545+jxsWWdrKiOhgk2UxwLZz1ncXsXnxZu0EBV/TiV6hVIZ8GIn8PnCNcNZkVhYdddcxfWM3z2P3wePWcnzA/XDmmQumwfV1H+oFVBcb3Yib4k869HPQ54Bu2CgvdtuGNdi050lyYdV/z7dgchyAH1s6ihVLRzNJG5jnDOSX4bWBes58uY9+JzkVSmXIB0ltjELsSk/Xg3T39PMdHlyWZFaIJ25+N+vYddjyHVlDJFnnwiDMK9t9sIk/meDs2EzVP7MqWCFEp0jdf849Pt1oWjthhZyDQgwZXtsz6mIjmWqoOgbFiSxNstPUaQZ43cR7ibwcZRtcfF1zGw6EJ7OyeBSuhLKeGBvJ0XCEEyKhOOWhiHmsrMgaKvIlNM2EpKsDlu33fIlQjiHzfcbWsYviZuelCVPP6BjBNFNzwdagZZCcyFJ45Byd5kFAiLfA9dxdfF1qwxeSzKJ4+a4t5QoiLkhVv1FwCWRlCZHkQdG5Dh+yhopC4/s+g8fVKfIlyRVsDa5N4bKQTk15w6vUM7p0dITsQ6DmRq96tmZBKQx5jO1UL8B9eEK2kln4urZJTRkqaqFwSZDaeo2qMdoe6IoQmJfSykQBetttPDZiPdxZwjvTM3W2/ooap2tppX7Ptci5ajsEgA+tXdkRyjHnemj1aN4wGPWMmn0IVhJ6+INkb3SUwpAPYtWlDVxvIXRhsk0gypMWsHe4oZDFW6e8H+p+zEvpZRUNqqfjQhZGEWX4s1BZXYbZxrl3hVRWLKng1z+dPTS174kTXYZcAnj2L990NsvwVY/afgvIPl9cz6juefeq0XYslMKQcwzkIGx7uN5CloXJPD9b0koAuGXL2o7z5rIBQrx12wPmqn7zbXuL8HS488H3Odf7oQuyz0CEXgeuI8NJaGfUmvOOxSW965ozVKcmIN984czpskQAdJTCkPsu/qCsoFxvITTOZzu/A0fqzqYJ1PdCrgt1PgA6Yp5qUbE9kEWHSWyGFuBR1HzXx/d+6IIc20C4OPD6MTkGP6+hIvXzhV09U4lrUXOG6tSUF5xntCwRAB2lMOS+iz9IKyjHWwiN81Hn5+vRGeO6mOdjM2429gzQio0Xyf6gDK2twbPtvH3Xx/d+6IIc20BMbV/vlDrWx8PRcsljqKa2r8fnHj7a5dkLAKNGY2hKXAsofs4A/me0jPUqpaEfmjSqMq+goXS3rOdXxHWh+M42zEtZ6ANJGVouC8J3fXzvh+rzUIYgq4GYnBjHcqIkfqVGl+NKQOc1VLbwzLxssZzMuT5L3KOi5wwHWXSX+o1SeOQ+lHEFDYnzZT2/Iq5LyCJQ9PXPK7zkuz6+90MTb6E7MU6cf2m1grOWsnidwm+Ok2qWnMdQudgntoKg0ER6L5E3odoPDIUhH4TKvCKR9fyKuC5kLBTdjY9Dfyc0YU2Nhauf7rs+nOsXsiCHGAhufoPybM3XbSGyXlQgA3bjvG3DGpauTL8wyFRDG4bCkJdxBQ1B1vMr4rpQxs2XePUhS2KWGgtXP913fYq4fjaDaiuW4eY3su66Yhsq1wJvGudQXZkEP4RkdjaJic2bN8vDhw/3/HezYhCojYOEIq4HJVxl66hT9Fh6BRu/21eMZSp9uo7Ry+vg4qqbqoxZ73UCIIQ4IqXcbL6eyyMXQuwDcD2A8wBeBPBPpZSzeY45aBgUauMgoYhtZx5Z2rLeB5fXzfW0B2U3qo+jPtvoCLXlpW3qKPPCXSTysla+CeAnpZQfBPBnAO7KP6TBgk+UqGj4OvsMC2IzOsoAl0ELYU64GF29hBrH+FjNKeaW9V4XIUo3LMhlyKWUT0opL7T/fBbAe/MPabDQT2pjrybuICwWRVG+BuHcKLgMWihFdZAQm7ap0G+napARM9n5zwDsj3i8gUA/qY15C3o421AqdHT45KlcyctQFBEiGPSwmI8VU6awkT7XfFINWe91EU7VsIRqvIZcCPGHAN5jeevzUsqvtT/zeQAXADzoOM5tAG4DgLVr12YabD/QT2pj3lgix4hRiwWnYUVsxDZcg1Txa8OgxLfzgiNfnIe2qZDXqfLpFQ3aQh8CryGXUn7M9b4Q4pcAfBLAR6WDAiOlfADAA0CLtRI4zr6hnw9bnonLNWLUosBtRjzI6FVYLI9XVyavm4JPvjjWM5PHqeJKS5RxngP5WSsfB/CvAfwDKeXZOEMaPPTrYcszcblGjKvD4TrmoKIXYbFBD9/0Annki0OQx6kKkZZQyoxl2inljZH/FoClAL4pWjXBz0op/3nuUSUAyDdxuUbMtlhwmxUUjbwPUy+qBwc9fNML9DKPlNWpCnFClDJjnsW51wtBLkMupfy7sQaSYEfWicv15m2LhU3r3PbdIidrXk+3V9WDZRNsKwIx80hFzakQaQmbMmMoyaDXu7ShKNFP6EaIN29bLDZfvtrbcKHIyZrX06W20r6u9KEoo2BbbMTKIxU5p0KkJag2inkaeRS9S0uGfIiRJ7bv+27RkzWvp9srT3nYBdu4iJFHCplToZ57yGKTV5mRmmP12Qau2HmokFBLMuQJmVC0oczr6camqrl2M0D5KYSDAO6cyuq5cxebvIuzi0CgF/b5xhuC0jSWSBgsFF1Sn7fSM8/3QytqB6VEvuzgzqmiKzzzVtVyGnnErkhNHnlCJhQdUsjr6camqi02Jko/UGTz8lDkDUsCF+eei+YYC8mQJ2RCL0IKeeOusalqi4mJAvSeQsedU2VIMOtzj5LtjTneZMgTMmMYqhJtKIOh4CKrMe5XoRNnTpUtwdyL8SZDXiD6WR1Wtsq0QULZDAWFPMaYCi/tPni87/OqbAnmXow3dQgqCP3s3DIoXWPKjGFYCPN04rli5yEytqsjzaveopAOQQk0+pkwS8m6/BiGsFGeWD9XgyfNq8FAoh8WhH4mzFKyLgHIRxHlUOgU0rzqP5IhLwj9bF22GNumJXQjD5fexqVetbxq/WyaV/1HMuQFoajWZYP+2wmDg7yFLWah067rN6Z5NaBIMfKC0M/Metmy+gnFIWasP82rwUVirSQkJCSUBIm1QmAYaGYJCQmLG4vakKc2XQkcpMU+YdCxqJOdRauoJZQfoUqICQn9wKI25IlvneBDWuwTyoBFbcgT3zrBh7TYJ5QBi9qQJ751gg9psU8oAxa1Ic9bMJEw/EiLfUIZsKhZK8BwiCMlFIdUBJNQBuQy5EKIfwfgUwDmAfwQwC9LKV+LMbCEhEFBWuwTBh15Qyv7pJQflFJuAvB1AF/IP6SEhISEhBDk8sillD/S/lwBsLToExISEvqCYS3uyh0jF0L8OoBfBHAawDbH524DcBsArF27Nu/PJiQMHYbVyAwKhrmS2yuaJYT4QwDvsbz1eSnl17TP3QVgmZRyl+9Hk2hWQkInUnu+4pGn9d2gILNolpTyY8zf+F8ADgHwGvKEhIROpPZ8xWOYi7tyJTuFED+h/XkDgBfyDSchYXFimI3MoGCYi7vyslb2CiG+K4T4DoBrAfxahDElJCw6DLORGRQMc3FXXtbKTbEGkpCwmDG1fb01Rj4MRmZQMMzFXYu+sjMhYRAwzEZmkDCsxV3JkCckDAiG1cgkFI9FLZqVkJCQMAxIhjwhISGh5EiGPCEhIaHkSIY8ISEhoeRIhjwhISGh5PBqrRTyo0K8AeBkxq+/G8BfRxxOGZDOeXEgnfPiQJ5zvlxKucZ8sS+GPA+EEIdtojHDjHTOiwPpnBcHijjnFFpJSEhIKDmSIU9ISEgoOcpoyB/o9wD6gHTOiwPpnBcHop9z6WLkCQkJCQmdKKNHnpCQkJCgIRnyhISEhJKjVIZcCPFxIcQJIcRfCCF29ns8MSCEuEwI8bQQ4vtCiONCiF9rv75aCPFNIcSft/+/SvvOXe1rcEIIsb1/o88HIURFCDEjhPh6+++hPmchxJgQ4lEhxAvt+/3Ti+Cc72jP6+8KIR4SQiwbtnMWQvyOEOKHQojvaq8Fn6MQ4mohxPPt9/6TEEKwByGlLMV/ACoAXgTwPgBLABwD8IF+jyvCeV0C4EPtf/8YgD8D8AEA/wHAzvbrOwH8+/a/P9A+96UArmhfk0q/zyPjuX8OrV6vX2//PdTnDOB3Afxq+99LAIwN8zkDGAfwAwC19t8PA/jlYTtnAD8L4EMAvqu9FnyOAP4UwE8DEAD+AMA/5o6hTB75hwH8hZTyL6WU5wH8PoBP9XlMuSGlfF1K+e32v/8WwPfRegA+hdaDj/b/J9v//hSA35dSvi2l/AGAv0Dr2pQKQoj3ArgOwG9rLw/tOQsh3onWA/9lAJBSnpdSzmKIz7mNUQA1IcQogOUAXsOQnbOU8o8AnDJeDjpHIcQlAN4ppfwT2bLqv6d9x4syGfJxAK9of7/afm1oIIRYB2ACwHMA/o6U8nWgZewB/Hj7Y8NyHe4H8K8AzGuvDfM5vw/AGwD+Rzuc9NtCiBUY4nOWUtYB/EcALwN4HcBpKeWTGOJz1hB6juPtf5uvs1AmQ26LFw0Nd1II8Q4ABwDcLqX8keujltdKdR2EEJ8E8EMp5RHuVyyvleqc0fJMPwTgv0opJwCcQWvLTaH059yOC38KrRDCpQBWCCFudX3F8lqpzpkB6hxznXuZDPmrAC7T/n4vWtu00kMIUUXLiD8opXys/fL/a2+30P7/D9uvD8N12ArgBiHES2iFyK4RQnwFw33OrwJ4VUr5XPvvR9Ey7MN8zh8D8AMp5RtSyiaAxwD8DIb7nBVCz/HV9r/N11kokyH/FoCfEEJcIYRYAuDnARzs85hyo52Z/jKA70spf0N76yCAX2r/+5cAfE17/eeFEEuFEFcA+Am0kiSlgZTyLinle6WU69C6j09JKW/FcJ/zXwF4RQixvv3SRwF8D0N8zmiFVLYIIZa35/lH0coBDfM5KwSdYzv88rdCiC3ta/WL2nf86HfGNzA7/Am0WB0vAvh8v8cT6Zz+PlpbqO8AONr+7xMA3gXgfwP48/b/V2vf+Xz7GpxAQGZ7EP8D8A9xkbUy1OcMYBOAw+17PQ1g1SI45z0AXgDwXQD/Ey22xlCdM4CH0MoBNNHyrH8lyzkC2Ny+Ti8C+C20K+85/6US/YSEhISSo0yhlYSEhIQEC5IhT0hISCg5kiFPSEhIKDmSIU9ISEgoOZIhT0hISCg5kiFPSEhIKDmSIU9ISEgoOf4/s8XgiE9QI+IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, \"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "altered-polymer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  7.,  27.,  85., 161., 235., 237., 165.,  64.,  15.,   4.]),\n",
       " array([-3.09730862, -2.46438094, -1.83145326, -1.19852558, -0.56559791,\n",
       "         0.06732977,  0.70025745,  1.33318513,  1.9661128 ,  2.59904048,\n",
       "         3.23196816]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "final-proceeding",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = rnd.gumbel(size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "innovative-student",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 38., 218., 322., 217., 115.,  50.,  28.,   6.,   4.,   2.]),\n",
       " array([-2.1885189 , -1.25522222, -0.32192555,  0.61137113,  1.54466781,\n",
       "         2.47796448,  3.41126116,  4.34455783,  5.27785451,  6.21115118,\n",
       "         7.14444786]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "roman-drain",
   "metadata": {},
   "source": [
    "## Operações vetorizadas e broadcasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "comprehensive-purchase",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0,1,num=51)\n",
    "y = np.sin(2*np.pi*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "twelve-primary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f051911c1f0>]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "female-montreal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f05190f3d90>]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2*np.cos(6*x) + np.exp(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "textile-volunteer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.  , 1.02, 1.04, 1.06, 1.08, 1.1 , 1.12, 1.14, 1.16, 1.18, 1.2 ,\n",
       "       1.22, 1.24, 1.26, 1.28, 1.3 , 1.32, 1.34, 1.36, 1.38, 1.4 , 1.42,\n",
       "       1.44, 1.46, 1.48, 1.5 , 1.52, 1.54, 1.56, 1.58, 1.6 , 1.62, 1.64,\n",
       "       1.66, 1.68, 1.7 , 1.72, 1.74, 1.76, 1.78, 1.8 , 1.82, 1.84, 1.86,\n",
       "       1.88, 1.9 , 1.92, 1.94, 1.96, 1.98, 2.  ])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "identified-market",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.04, 0.08, 0.12, 0.16, 0.2 , 0.24, 0.28, 0.32, 0.36, 0.4 ,\n",
       "       0.44, 0.48, 0.52, 0.56, 0.6 , 0.64, 0.68, 0.72, 0.76, 0.8 , 0.84,\n",
       "       0.88, 0.92, 0.96, 1.  , 1.04, 1.08, 1.12, 1.16, 1.2 , 1.24, 1.28,\n",
       "       1.32, 1.36, 1.4 , 1.44, 1.48, 1.52, 1.56, 1.6 , 1.64, 1.68, 1.72,\n",
       "       1.76, 1.8 , 1.84, 1.88, 1.92, 1.96, 2.  ])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "occasional-complaint",
   "metadata": {},
   "source": [
    "### Outros tipos de indexações\n",
    "\n",
    "#### Indexando usando variáveis lógicas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fitted-psychiatry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.77997741,  1.04347792, -2.54628676,  1.24286073, -1.69926903,\n",
       "       -1.24987317,  2.38368852, -0.19999258,  1.58995438,  0.4589341 ])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = rnd.randn(10)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "smaller-disco",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False,  True, False, False,  True, False,  True,\n",
       "        True])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positivo = x > 0\n",
    "positivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "skilled-version",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77997741, 1.04347792, 1.24286073, 2.38368852, 1.58995438,\n",
       "       0.4589341 ])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[positivo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "provincial-sequence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.54628676, -1.69926903, -1.24987317, -0.19999258])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[~positivo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "subject-patch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.51323665e+00, 6.60425522e-01, 7.81835392e-02, 1.76947247e-01,\n",
       "       2.71410749e-01, 3.87662914e-01, 6.00594630e-01, 7.12319894e-01,\n",
       "       1.44725779e+00, 1.34976924e+00, 8.69802835e-02, 9.76557367e-01,\n",
       "       2.84019557e-05, 5.88129534e-01, 1.10595408e+00, 4.94789833e-01,\n",
       "       2.29261008e-01, 1.37328334e+00])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = rnd.randn(5,8)\n",
    "y[y > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surprising-japan",
   "metadata": {},
   "source": [
    "#### Já tínhamos visto fatiamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "concerned-visit",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(range(50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "committed-maryland",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 15, 20])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[ [10,15,20]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unauthorized-entity",
   "metadata": {},
   "source": [
    "## Álgebra linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "sharp-oracle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.61369072, -0.74715209, -1.18625723, -0.09583723, -1.42886673],\n",
       "       [-0.67965437,  1.63275252, -2.5878914 ,  0.59475804, -1.02750672],\n",
       "       [ 1.07161319, -0.51377731, -0.45160769,  0.59791653, -2.45569912],\n",
       "       [-0.94591804, -0.0476593 ,  0.7482287 ,  2.31135166,  0.00738173],\n",
       "       [ 0.06073121, -1.49248878,  1.10102178,  1.19930112, -1.74138814]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A0 = rnd.randn(5,5)\n",
    "A0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "neither-boost",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10.,  0.,  0.,  0.,  0.],\n",
       "       [ 0., 10.,  0.,  0.,  0.],\n",
       "       [ 0.,  0., 10.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., 10.,  0.],\n",
       "       [ 0.,  0.,  0.,  0., 10.]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Matriz diagonal com diagonal = 10\n",
    "A1 = np.diag(10*np.ones(5))\n",
    "A1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "superior-cabin",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = A0 + A1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "amazing-freeware",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.ones(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "passing-myrtle",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy.linalg as linalg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "inappropriate-variable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.13280483, 0.12639701, 0.12080103, 0.08450851, 0.11457434])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resolvendo o sistema linear A*x = b\n",
    "x = linalg.solve(A, b)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "acute-valuable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(A,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "fiscal-catering",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "suffering-appliance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.06136907e+01, -6.79654373e-01,  1.07161319e+00,\n",
       "        -9.45918036e-01,  6.07312127e-02],\n",
       "       [-7.47152094e-01,  1.16327525e+01, -5.13777312e-01,\n",
       "        -4.76593049e-02, -1.49248878e+00],\n",
       "       [-1.18625723e+00, -2.58789140e+00,  9.54839231e+00,\n",
       "         7.48228700e-01,  1.10102178e+00],\n",
       "       [-9.58372280e-02,  5.94758040e-01,  5.97916531e-01,\n",
       "         1.23113517e+01,  1.19930112e+00],\n",
       "       [-1.42886673e+00, -1.02750672e+00, -2.45569912e+00,\n",
       "         7.38173448e-03,  8.25861186e+00]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Matriz transposta\n",
    "A.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "another-tanzania",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inversa da matriz\n",
    "iA = linalg.inv(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "reverse-stick",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00,  0.00000000e+00,  6.93889390e-18,\n",
       "         0.00000000e+00, -5.55111512e-17],\n",
       "       [-7.37257477e-18,  1.00000000e+00, -3.12250226e-17,\n",
       "        -6.93889390e-18, -2.77555756e-17],\n",
       "       [ 1.30104261e-18, -1.38777878e-17,  1.00000000e+00,\n",
       "         6.93889390e-18, -2.77555756e-17],\n",
       "       [-4.73322011e-18, -6.50521303e-19, -1.31459513e-17,\n",
       "         1.00000000e+00, -2.16840434e-19],\n",
       "       [-8.67361738e-19,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  1.00000000e+00]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(iA,A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "intense-afghanistan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119920.46580368283"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determinante da matriz\n",
    "linalg.det(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "asian-occasions",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119920.46580368285"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 / linalg.det(iA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "alleged-irrigation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vamos criar uma matriz simétrica\n",
    "B = A + A.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "bronze-communist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[21.22738143, -1.42680647, -0.11464403, -1.04175526, -1.36813551],\n",
       "       [-1.42680647, 23.26550503, -3.10166871,  0.54709873, -2.5199955 ],\n",
       "       [-0.11464403, -3.10166871, 19.09678463,  1.34614523, -1.35467734],\n",
       "       [-1.04175526,  0.54709873,  1.34614523, 24.62270331,  1.20668286],\n",
       "       [-1.36813551, -2.5199955 , -1.35467734,  1.20668286, 16.51722371]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "sensitive-guidance",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.60731825,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [-0.30968264,  4.81348124,  0.        ,  0.        ,  0.        ],\n",
       "       [-0.02488303, -0.64597209,  4.32190762,  0.        ,  0.        ],\n",
       "       [-0.22610881,  0.09911262,  0.32498218,  4.94531509,  0.        ],\n",
       "       [-0.29694834, -0.54263331, -0.39625842,  0.26734372,  3.98824536]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.cholesky(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interesting-discrimination",
   "metadata": {},
   "source": [
    "# [Scipy](https://scipy.org/)\n",
    "\n",
    "Pacote construído sobre o numpy que tem um monte de funcionalidade.\n",
    "\n",
    "Em particular essa biblioteca tem as seguintes áreas:\n",
    "\n",
    " * [Funções especiais](https://docs.scipy.org/doc/scipy/tutorial/special.html)\n",
    " * [Integração numérica (funções e EDO)](https://docs.scipy.org/doc/scipy/tutorial/integrate.html)\n",
    " * [Interpolação](https://docs.scipy.org/doc/scipy/tutorial/interpolate.html)\n",
    " * [Optimização](https://docs.scipy.org/doc/scipy/tutorial/optimize.html)\n",
    " * [Transformadas de Fourier](https://docs.scipy.org/doc/scipy/tutorial/fft.html)\n",
    " * [Processamento de sinais](https://docs.scipy.org/doc/scipy/tutorial/signal.html)\n",
    " * [Álgebra linear](https://docs.scipy.org/doc/scipy/tutorial/linalg.html)\n",
    " * [Problemas de autovalor esparsos com ARPACK](https://docs.scipy.org/doc/scipy/tutorial/arpack.html)\n",
    " * [Rotinas para grafos esparsos](https://docs.scipy.org/doc/scipy/tutorial/csgraph.html)\n",
    " * [Estruturas espaciais](https://docs.scipy.org/doc/scipy/tutorial/spatial.html)\n",
    " * [Estatística](https://docs.scipy.org/doc/scipy/tutorial/stats.html)\n",
    " * [Imagens multidimensionais](https://docs.scipy.org/doc/scipy/tutorial/ndimage.html)\n",
    " * [Entrada e saída](https://docs.scipy.org/doc/scipy/tutorial/io.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "coated-pixel",
   "metadata": {},
   "source": [
    "\n",
    "## Álgebra linear\n",
    "\n",
    "Bem mais completo que o `numpy`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "fiscal-training",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.linalg as lin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "missing-exclusive",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Problema de autovalor\n",
    "λ, ϕ = lin.eig(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "classical-trailer",
   "metadata": {},
   "outputs": [],
   "source": [
    "Λ = np.diag(λ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "stuffed-civilization",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.552713678800501e-15"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 3\n",
    "np.abs(B.dot(ϕ[:,i]) - λ[i] * ϕ[:,i]).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "fluid-usage",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decomposição LU\n",
    "P,L,U = lin.lu(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "subject-lebanon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "plastic-burton",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [-0.06403563,  1.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [ 0.10096518, -0.03783724,  1.        ,  0.        ,  0.        ],\n",
       "       [-0.08912244, -0.00986174,  0.06441021,  1.        ,  0.        ],\n",
       "       [ 0.00572197, -0.12846141,  0.08002273,  0.09985789,  1.        ]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "complex-plant",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10.61369072, -0.74715209, -1.18625723, -0.09583723, -1.42886673],\n",
       "       [ 0.        , 11.58490816, -2.66385413,  0.58862104, -1.11900511],\n",
       "       [ 0.        ,  0.        ,  9.5673701 ,  0.62986455, -2.3537734 ],\n",
       "       [ 0.        ,  0.        ,  0.        , 12.26804553,  0.02060935],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  8.30933618]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "municipal-invasion",
   "metadata": {},
   "outputs": [],
   "source": [
    "lu, piv = lin.lu_factor(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "extreme-precipitation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.06136907e+01, -7.47152094e-01, -1.18625723e+00,\n",
       "        -9.58372280e-02, -1.42886673e+00],\n",
       "       [-6.40356301e-02,  1.15849082e+01, -2.66385413e+00,\n",
       "         5.88621042e-01, -1.11900511e+00],\n",
       "       [ 1.00965180e-01, -3.78372414e-02,  9.56737010e+00,\n",
       "         6.29864550e-01, -2.35377340e+00],\n",
       "       [-8.91224420e-02, -9.86173757e-03,  6.44102112e-02,\n",
       "         1.22680455e+01,  2.06093500e-02],\n",
       "       [ 5.72196932e-03, -1.28461406e-01,  8.00227288e-02,\n",
       "         9.98578873e-02,  8.30933618e+00]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "identified-birmingham",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.13280483, 0.12639701, 0.12080103, 0.08450851, 0.11457434])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin.lu_solve( (lu,piv), b) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "experienced-matter",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "touched-mapping",
   "metadata": {},
   "source": [
    "## Integração numérica (funções)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "historical-aurora",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.integrate as integr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "occupational-advancement",
   "metadata": {},
   "source": [
    "Quero calcular a seguinte integral:\n",
    "\n",
    "$$\n",
    "\\int_0^1 \\cos(x) \\: dx = \\sin(1)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "closing-rates",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.841470984807879"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I1 = integr.romberg(np.cos, 0, 1)\n",
    "I1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "featured-morocco",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8414709848078965"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ie = np.sin(1)\n",
    "Ie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "golden-armenia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.7541523789077473e-14"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "erro = I1 - Ie\n",
    "erro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "identical-import",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funçãosinha bem chata para integrar na mão...\n",
    "def minhafun(x):\n",
    "    return np.cos(x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "serious-juvenile",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9045242379007594"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integr.romberg(minhafun, 0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beneficial-eugene",
   "metadata": {},
   "source": [
    "# Exemplo Camada limite\n",
    "\n",
    "A camada limite foi medida no túnel de vento. Vamos analisar estes dados.\n",
    "Os dados estão armazenados em um arquivo no format HDF5, um formato binário\n",
    "adequado para armazenar arrays de grandes dimensões"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "double-cambridge",
   "metadata": {},
   "source": [
    "## Ler os dados de um arquivo HDF5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "confidential-teacher",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carregar a biblioteca python para ler arquivos HDF5:\n",
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "accurate-prize",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Abrir o arquivo:\n",
    "f = h5py.File(\"camada-limite.h5\", \"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "medieval-laser",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KeysViewHDF5 ['U', 'z']>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vamos ver o que tem dentro deste arquivo\n",
    "f.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "impossible-institute",
   "metadata": {},
   "outputs": [],
   "source": [
    "U = f['U'][:]\n",
    "z = f['z'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "beautiful-youth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 80000)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "committed-voltage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39,)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "orange-pizza",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vamos ler a taxa de amostragem a partir dos atributos de U\n",
    "fs = f['U'].attrs['rate'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "found-album",
   "metadata": {},
   "source": [
    "## Plotar os perfis de velocidade\n",
    "Vamos plotar o perfil de velocidades e intensidade de turbulência:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "tired-trust",
   "metadata": {},
   "outputs": [],
   "source": [
    "Um = U.mean(1)\n",
    "sU = U.std(1)\n",
    "I = sU / Um * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "liable-wildlife",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Altura (m)')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(Um, z, \"o\")\n",
    "plt.xlabel(\"Velocidade (m/s)\")\n",
    "plt.ylabel(\"Altura (m)\")\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(I, z, \"o\")\n",
    "plt.xlabel(\"Intensidade de turb. (%)\")\n",
    "plt.ylabel(\"Altura (m)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "later-government",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Turbulência')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(12,8))\n",
    "ax1.plot(Um, z, \"o\")\n",
    "ax1.grid()\n",
    "ax1.set_xlabel(\"Velocidade (m/s)\")\n",
    "ax1.set_ylabel(\"Altura (m)\")\n",
    "ax1.set_title(\"Perfil de velocidade\")\n",
    "ax2.plot(I, z, \"o\")\n",
    "ax2.grid()\n",
    "ax2.set_xlabel(\"Intensidade de turb. (%)\")\n",
    "ax2.set_title(\"Turbulência\")\n",
    "#plt.ylabel(\"Altura (m)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "limiting-lesbian",
   "metadata": {},
   "source": [
    "## Espectro de turbulência"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "bearing-syndicate",
   "metadata": {},
   "outputs": [],
   "source": [
    "iref = 14\n",
    "zref = z[iref]\n",
    "u = U[iref]\n",
    "N = len(u)\n",
    "dt = 1/fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "published-clock",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.arange(N) * dt\n",
    "ū = u.mean()\n",
    "σ = u.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "criminal-figure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Velocidade (m/s)')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(t, u, label=\"$U(t)$\", color='k')\n",
    "plt.axhline(y=ū, linewidth=3, color='g', label=\"$\\\\bar{U}$\")\n",
    "plt.axhline(y = ū+2*σ, color='r', linestyle='--', linewidth=2, label='$\\\\bar{U} + 2\\\\sigma$')\n",
    "plt.axhline(y = ū-2*σ, color='b', linestyle='--', linewidth=2, label='$\\\\bar{U} - 2\\\\sigma$')\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel(\"Tempo (s)\")\n",
    "plt.ylabel(\"Velocidade (m/s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "motivated-buyer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Velocidade (m/s)')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.xlim( (18,20))\n",
    "plt.plot(t, u, label=\"$U(t)$\", color='k')\n",
    "plt.axhline(y=ū, linewidth=3, color='g', label=\"$\\\\bar{U}$\")\n",
    "plt.axhline(y = ū+2*σ, color='r', linestyle='--', linewidth=2, label='$\\\\bar{U} + 2\\\\sigma$')\n",
    "plt.axhline(y = ū-2*σ, color='b', linestyle='--', linewidth=2, label='$\\\\bar{U} - 2\\\\sigma$')\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel(\"Tempo (s)\")\n",
    "plt.ylabel(\"Velocidade (m/s)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "liberal-contents",
   "metadata": {},
   "source": [
    "## Análise espectral da velocidade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "sunrise-mediterranean",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.signal as signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "growing-lebanon",
   "metadata": {},
   "outputs": [],
   "source": [
    "f, S = signal.welch(u, fs, scaling='spectrum', nperseg=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "fundamental-thailand",
   "metadata": {},
   "outputs": [],
   "source": [
    "f1 = f[1:]\n",
    "S1 = S[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "productive-cherry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f050c9ff5e0>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.grid()\n",
    "plt.loglog(f1, S1, label=\"Medido\")\n",
    "plt.xlabel(\"Frequência (Hz)\")\n",
    "plt.ylabel(\"PSD\")\n",
    "ff = 10.0\n",
    "ss = 1.0\n",
    "a = ss / (ff**(-5/3))\n",
    "plt.plot(f1, a*f1**(-5/3), \"r--\", label=\"Espectro de Kolmogorov $f^{-5/3}$\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "healthy-compression",
   "metadata": {},
   "source": [
    "# Exemplo: Equações de Lorenz\n",
    "\n",
    "<https://jupyter.org/try-jupyter/retro/notebooks/?path=notebooks/Lorenz.ipynb>\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\dot{x} & = \\sigma(y-x) \\\\\n",
    "\\dot{y} & = \\rho x - y - xz \\\\\n",
    "\\dot{z} & = -\\beta z + xy\n",
    "\\end{aligned}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "understanding-ballot",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import integrate\n",
    "np.random.seed(1)\n",
    "x0 = -15 + 30 * np.random.random((N, 3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "designed-documentary",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "billion-cardiff",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def lorenz_deriv(x_y_z, t0, sigma=10.0, beta=8./3., rho=28.0):\n",
    "    \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n",
    "    x, y, z = x_y_z\n",
    "    return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "better-dragon",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_time = 100.0\n",
    "t = np.linspace(0, max_time, int(250*max_time))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "varying-edinburgh",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = integrate.odeint(lorenz_deriv, [0.5, 0.4, 0.6], t)\n",
    "x2 = integrate.odeint(lorenz_deriv, [0.50000001, 0.4, 0.6], t)                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "recognized-education",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f050c7d67c0>]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,10))\n",
    "\n",
    "plt.plot(t, x1[:,2])\n",
    "plt.plot(t, x2[:,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "healthy-bottle",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "\n",
    "Biblioteca para análise de dados. Dados em forma de tabelas. Pode-se pensar nisso como se fosse uma planilha.\n",
    "\n",
    "Referência: [Análise de dados com Python e Pandas](https://novatec.com.br/livros/analise-dados-com-python-pandas/) de Daniel Y. Chen\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "smooth-pilot",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "primary-distinction",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "conscious-choice",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('gapminder.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "continuing-leone",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3308</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "      <td>9216418</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3311</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3312</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3313 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year  lifeExp       pop   gdpPercap\n",
       "0     Afghanistan      Asia  1952   28.801   8425333  779.445314\n",
       "1     Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
       "2     Afghanistan      Asia  1962   31.997  10267083  853.100710\n",
       "3     Afghanistan      Asia  1967   34.020  11537966  836.197138\n",
       "4     Afghanistan      Asia  1972   36.088  13079460  739.981106\n",
       "...           ...       ...   ...      ...       ...         ...\n",
       "3308     Zimbabwe    Africa  1987   62.351   9216418  706.157306\n",
       "3309     Zimbabwe    Africa  1992   60.377  10704340  693.420786\n",
       "3310     Zimbabwe    Africa  1997   46.809  11404948  792.449960\n",
       "3311     Zimbabwe    Africa  2002   39.989  11926563  672.038623\n",
       "3312     Zimbabwe    Africa  2007   43.487  12311143  469.709298\n",
       "\n",
       "[3313 rows x 6 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "freelance-curve",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "marine-delight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3313, 6)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "understood-czech",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap'], dtype='object')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "czech-manitoba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country       object\n",
       "continent     object\n",
       "year           int64\n",
       "lifeExp      float64\n",
       "pop            int64\n",
       "gdpPercap    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "healthy-thong",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3313 entries, 0 to 3312\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   country    3313 non-null   object \n",
      " 1   continent  3313 non-null   object \n",
      " 2   year       3313 non-null   int64  \n",
      " 3   lifeExp    3313 non-null   float64\n",
      " 4   pop        3313 non-null   int64  \n",
      " 5   gdpPercap  3313 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(2)\n",
      "memory usage: 155.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "tired-beast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent  year  lifeExp       pop   gdpPercap\n",
       "0  Afghanistan      Asia  1952   28.801   8425333  779.445314\n",
       "1  Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
       "2  Afghanistan      Asia  1962   31.997  10267083  853.100710\n",
       "3  Afghanistan      Asia  1967   34.020  11537966  836.197138\n",
       "4  Afghanistan      Asia  1972   36.088  13079460  739.981106"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "compressed-explorer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3308</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "      <td>9216418</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3311</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3312</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent  year  lifeExp       pop   gdpPercap\n",
       "3308  Zimbabwe    Africa  1987   62.351   9216418  706.157306\n",
       "3309  Zimbabwe    Africa  1992   60.377  10704340  693.420786\n",
       "3310  Zimbabwe    Africa  1997   46.809  11404948  792.449960\n",
       "3311  Zimbabwe    Africa  2002   39.989  11926563  672.038623\n",
       "3312  Zimbabwe    Africa  2007   43.487  12311143  469.709298"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "straight-alliance",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_df = df['country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "tough-jerusalem",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Afghanistan\n",
       "1       Afghanistan\n",
       "2       Afghanistan\n",
       "3       Afghanistan\n",
       "4       Afghanistan\n",
       "           ...     \n",
       "3308       Zimbabwe\n",
       "3309       Zimbabwe\n",
       "3310       Zimbabwe\n",
       "3311       Zimbabwe\n",
       "3312       Zimbabwe\n",
       "Name: country, Length: 3313, dtype: object"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "prospective-ready",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(country_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "breeding-evening",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country      Afghanistan\n",
       "continent           Asia\n",
       "year                1952\n",
       "lifeExp           28.801\n",
       "pop              8425333\n",
       "gdpPercap        779.445\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pegar linhas de acordo com o rótulo\n",
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "skilled-console",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2002</td>\n",
       "      <td>42.129</td>\n",
       "      <td>25268405</td>\n",
       "      <td>726.734055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.828</td>\n",
       "      <td>31889923</td>\n",
       "      <td>974.580338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1952</td>\n",
       "      <td>55.230</td>\n",
       "      <td>1282697</td>\n",
       "      <td>1601.056136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1957</td>\n",
       "      <td>59.280</td>\n",
       "      <td>1476505</td>\n",
       "      <td>1942.284244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1962</td>\n",
       "      <td>64.820</td>\n",
       "      <td>1728137</td>\n",
       "      <td>2312.888958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1967</td>\n",
       "      <td>66.220</td>\n",
       "      <td>1984060</td>\n",
       "      <td>2760.196931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1972</td>\n",
       "      <td>67.690</td>\n",
       "      <td>2263554</td>\n",
       "      <td>3313.422188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1977</td>\n",
       "      <td>68.930</td>\n",
       "      <td>2509048</td>\n",
       "      <td>3533.003910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1982</td>\n",
       "      <td>70.420</td>\n",
       "      <td>2780097</td>\n",
       "      <td>3630.880722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1987</td>\n",
       "      <td>72.000</td>\n",
       "      <td>3075321</td>\n",
       "      <td>3738.932735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1992</td>\n",
       "      <td>71.581</td>\n",
       "      <td>3326498</td>\n",
       "      <td>2497.437901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country continent  year  lifeExp       pop    gdpPercap\n",
       "10  Afghanistan      Asia  2002   42.129  25268405   726.734055\n",
       "11  Afghanistan      Asia  2007   43.828  31889923   974.580338\n",
       "12      Albania    Europe  1952   55.230   1282697  1601.056136\n",
       "13      Albania    Europe  1957   59.280   1476505  1942.284244\n",
       "14      Albania    Europe  1962   64.820   1728137  2312.888958\n",
       "15      Albania    Europe  1967   66.220   1984060  2760.196931\n",
       "16      Albania    Europe  1972   67.690   2263554  3313.422188\n",
       "17      Albania    Europe  1977   68.930   2509048  3533.003910\n",
       "18      Albania    Europe  1982   70.420   2780097  3630.880722\n",
       "19      Albania    Europe  1987   72.000   3075321  3738.932735\n",
       "20      Albania    Europe  1992   71.581   3326498  2497.437901"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[10:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "linear-horror",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country      Afghanistan\n",
       "continent           Asia\n",
       "year                1952\n",
       "lifeExp           28.801\n",
       "pop              8425333\n",
       "gdpPercap        779.445\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pegar linhas de acordo com o índice\n",
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "serious-supervision",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2002</td>\n",
       "      <td>42.129</td>\n",
       "      <td>25268405</td>\n",
       "      <td>726.734055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.828</td>\n",
       "      <td>31889923</td>\n",
       "      <td>974.580338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1952</td>\n",
       "      <td>55.230</td>\n",
       "      <td>1282697</td>\n",
       "      <td>1601.056136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1957</td>\n",
       "      <td>59.280</td>\n",
       "      <td>1476505</td>\n",
       "      <td>1942.284244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1962</td>\n",
       "      <td>64.820</td>\n",
       "      <td>1728137</td>\n",
       "      <td>2312.888958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1967</td>\n",
       "      <td>66.220</td>\n",
       "      <td>1984060</td>\n",
       "      <td>2760.196931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1972</td>\n",
       "      <td>67.690</td>\n",
       "      <td>2263554</td>\n",
       "      <td>3313.422188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1977</td>\n",
       "      <td>68.930</td>\n",
       "      <td>2509048</td>\n",
       "      <td>3533.003910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1982</td>\n",
       "      <td>70.420</td>\n",
       "      <td>2780097</td>\n",
       "      <td>3630.880722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1987</td>\n",
       "      <td>72.000</td>\n",
       "      <td>3075321</td>\n",
       "      <td>3738.932735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country continent  year  lifeExp       pop    gdpPercap\n",
       "10  Afghanistan      Asia  2002   42.129  25268405   726.734055\n",
       "11  Afghanistan      Asia  2007   43.828  31889923   974.580338\n",
       "12      Albania    Europe  1952   55.230   1282697  1601.056136\n",
       "13      Albania    Europe  1957   59.280   1476505  1942.284244\n",
       "14      Albania    Europe  1962   64.820   1728137  2312.888958\n",
       "15      Albania    Europe  1967   66.220   1984060  2760.196931\n",
       "16      Albania    Europe  1972   67.690   2263554  3313.422188\n",
       "17      Albania    Europe  1977   68.930   2509048  3533.003910\n",
       "18      Albania    Europe  1982   70.420   2780097  3630.880722\n",
       "19      Albania    Europe  1987   72.000   3075321  3738.932735"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[10:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "cleared-finnish",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "-1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pandas/core/indexes/range.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    354\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_range\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: -1 is not in range",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_10036/1145300078.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    877\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    878\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 879\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    880\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    881\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1108\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1109\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1110\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1112\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_slice_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m   1057\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1058\u001b[0m         \u001b[0;31m# GH#5667 this will fail if the label is not present in the axis.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1059\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1060\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1061\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_handle_lowerdim_multi_index_axis0\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mxs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   3491\u001b[0m             \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3493\u001b[0;31m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3495\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pandas/core/indexes/range.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    355\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_range\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: -1"
     ]
    }
   ],
   "source": [
    "df.loc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "digital-education",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country      Zimbabwe\n",
       "continent      Africa\n",
       "year             2007\n",
       "lifeExp        43.487\n",
       "pop          12311143\n",
       "gdpPercap     469.709\n",
       "Name: 3312, dtype: object"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "written-overhead",
   "metadata": {},
   "source": [
    "## Agrupando e agregando dados\n",
    "\n",
    "Perguntas.\n",
    "\n",
    " 1. Para cada ano qual era a expectativa de vida média\n",
    " 2. Podemos ver isso por continente?\n",
    " 3. Quantos países estão listados por continente?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "heated-serbia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1950    62.002568\n",
       "1951    65.904167\n",
       "1952    49.170708\n",
       "1953    66.674563\n",
       "1954    67.459817\n",
       "1955    67.806757\n",
       "1956    67.950637\n",
       "1957    51.614590\n",
       "1958    68.815936\n",
       "1959    68.226579\n",
       "1960    68.470837\n",
       "1961    68.862480\n",
       "1962    54.035234\n",
       "1963    69.595735\n",
       "1964    70.063105\n",
       "1965    70.259881\n",
       "1966    70.447526\n",
       "1967    56.263629\n",
       "1968    70.689081\n",
       "1969    70.653896\n",
       "1970    70.961141\n",
       "1971    71.103976\n",
       "1972    58.474481\n",
       "1973    71.500338\n",
       "1974    71.778504\n",
       "1975    71.939218\n",
       "1976    72.158050\n",
       "1977    60.429090\n",
       "1978    72.717567\n",
       "1979    73.018717\n",
       "1980    73.064524\n",
       "1981    73.337399\n",
       "1982    62.365871\n",
       "1983    73.787778\n",
       "1984    74.100741\n",
       "1985    74.112222\n",
       "1986    74.452222\n",
       "1987    63.984860\n",
       "1988    74.760000\n",
       "1989    74.924444\n",
       "1990    74.283437\n",
       "1991    74.374848\n",
       "1992    65.008443\n",
       "1993    74.324545\n",
       "1994    74.456667\n",
       "1995    74.552727\n",
       "1996    75.029394\n",
       "1997    65.873799\n",
       "1998    75.569697\n",
       "1999    75.703636\n",
       "2000    76.026364\n",
       "2001    76.257879\n",
       "2002    66.835695\n",
       "2003    76.586667\n",
       "2004    76.921563\n",
       "2005    76.718667\n",
       "2006    77.887778\n",
       "2007    67.868557\n",
       "Name: lifeExp, dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('year')['lifeExp'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "certain-joining",
   "metadata": {},
   "source": [
    "Vamos detalhar isso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "religious-patent",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.groupby.generic.DataFrameGroupBy"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_year_df = df.groupby('year')\n",
    "type(grouped_by_year_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "periodic-protection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.groupby.generic.SeriesGroupBy"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_year_lifeExp = grouped_by_year_df['lifeExp']\n",
    "type(grouped_by_year_lifeExp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "optional-tragedy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "3308    False\n",
       "3309    False\n",
       "3310    False\n",
       "3311    False\n",
       "3312    False\n",
       "Name: year, Length: 3313, dtype: bool"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = df['year'] == 1960\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "colored-hudson",
   "metadata": {},
   "outputs": [],
   "source": [
    "df60 = df[b]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "pediatric-layout",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82      70.89000\n",
       "138     68.78000\n",
       "261     69.65000\n",
       "405     69.18000\n",
       "510     71.04000\n",
       "612     31.63176\n",
       "756     70.46000\n",
       "814     72.21000\n",
       "996     69.01000\n",
       "1054    70.40000\n",
       "1296    68.09000\n",
       "1353    74.09000\n",
       "1484    69.21000\n",
       "1552    67.80000\n",
       "1776    69.07000\n",
       "2041    73.37000\n",
       "2116    71.26000\n",
       "2207    73.58000\n",
       "2341    67.86000\n",
       "2399    64.25000\n",
       "2613    70.35000\n",
       "2736    69.27000\n",
       "2839    73.04000\n",
       "2897    71.44000\n",
       "2967    64.40000\n",
       "3171    69.91000\n",
       "Name: lifeExp, dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df60['lifeExp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "secondary-cartridge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68.47083692307693"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df60['lifeExp'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prepared-frontier",
   "metadata": {},
   "source": [
    "Vamos fazer por continente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "graphic-personal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1950</th>\n",
       "      <th>Africa</th>\n",
       "      <td>41.361500</td>\n",
       "      <td>1422.081643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>59.405286</td>\n",
       "      <td>5567.245762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>53.675000</td>\n",
       "      <td>1363.645814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>65.755916</td>\n",
       "      <td>6804.996873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSU</th>\n",
       "      <td>59.950000</td>\n",
       "      <td>3638.203164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2007</th>\n",
       "      <th>Americas</th>\n",
       "      <td>73.400485</td>\n",
       "      <td>11940.901644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>70.660233</td>\n",
       "      <td>15338.057326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>77.299059</td>\n",
       "      <td>24174.152973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSU</th>\n",
       "      <td>69.457333</td>\n",
       "      <td>9522.539346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>71.315000</td>\n",
       "      <td>13156.978742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>286 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  lifeExp     gdpPercap\n",
       "year continent                         \n",
       "1950 Africa     41.361500   1422.081643\n",
       "     Americas   59.405286   5567.245762\n",
       "     Asia       53.675000   1363.645814\n",
       "     Europe     65.755916   6804.996873\n",
       "     FSU        59.950000   3638.203164\n",
       "...                   ...           ...\n",
       "2007 Americas   73.400485  11940.901644\n",
       "     Asia       70.660233  15338.057326\n",
       "     Europe     77.299059  24174.152973\n",
       "     FSU        69.457333   9522.539346\n",
       "     Oceania    71.315000  13156.978742\n",
       "\n",
       "[286 rows x 2 columns]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_group_var = df.groupby(['year', 'continent'])[['lifeExp', 'gdpPercap']].mean()\n",
    "multi_group_var"
   ]
  }
 ],
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