{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NumPy \n",
    "\n",
    "NumPy (ou Numpy) é uma biblioteca de álgebra linear para Python, a razão pela qual é tão importante para a Data Science com Python é que quase todas as bibliotecas dependem do NumPy como um dos seus principais blocos de construção.\n",
    "\n",
    "Numpy também é incrivelmente rápido, pois tem ligações para bibliotecas C. Para obter mais informações sobre por que você deseja usar Arrays em vez de listas, confira esta excelente publicação do [StackOverflow post](http://stackoverflow.com/questions/993984/why-numpy-instead-of-python-lists).\n",
    "\n",
    "Nós só aprenderemos os conceitos básicos do NumPy, para começar, precisamos instalá-lo!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instruções de instalação\n",
    "\n",
    "** É altamente recomendável que instale o Python usando a distribuição da Anaconda para garantir que todas as dependências subjacentes (como as bibliotecas de Álgebra Linear) se sincronizem com o uso de uma instalação conda. Se você tiver o Anaconda, instale o NumPy acessando seu terminal ou prompt de comando e digite: **\n",
    "\n",
    "    conda install numpy\n",
    "    \n",
    "** Se você não possui Anaconda e não pode instalá-lo, consulte: [Numpy's official documentation on various installation instructions.](http://docs.scipy.org/doc/numpy-1.10.1/user/install.html)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usando NumPy\n",
    "\n",
    "Depois de instalar o NumPy, você pode importá-lo como uma biblioteca:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy possui muitas funções e capacidades incorporadas. Não vamos cobri-los na totalidade, mas, em vez disso, vamos nos concentrar em alguns dos aspectos mais importantes de Numpy: vetores, arrays, matrizes e geração de números. Comecemos por arrays:\n",
    "\n",
    "# Numpy Arrays\n",
    "\n",
    "As matrizes de NumPy são a maneira principal de usar Numpy ao longo do curso. Numpy arrays essencialmente vêm de duas formas: vetores e matrizes. Os vetores são estritamente arranjos de 1d e as matrizes são 2d (mas você deve observar que uma matriz ainda pode ter apenas uma linha ou uma coluna).\n",
    "\n",
    "Vamos começar nossa introdução explorando como criar matrizes numPy.\n",
    "\n",
    "\n",
    "## Criando NumPy Arrays\n",
    "\n",
    "### De uma lista de Python\n",
    "\n",
    "Podemos criar uma matriz convertendo diretamente uma lista ou lista de listas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minha_lista = [1,2,3]\n",
    "minha_lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(minha_lista)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minha_matriz = [[1,2,3],[4,5,6],[7,8,9]]\n",
    "minha_matriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(minha_matriz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Métodos incorporados (Built-in Methods)\n",
    "\n",
    "Há muitas maneiras embutidas de gerar Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### arange\n",
    "\n",
    "Retorna valores uniformemente espaçados dentro de um determinado intervalo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,11,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### zeros e ones\n",
    "\n",
    "Gerar matrizes de zeros ou de ums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### linspace\n",
    "Retorna números uniformemente espaçados ao longo de um intervalo especificado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  2.5,  5. ,  7.5, 10. ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,10,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.20408163,  0.40816327,  0.6122449 ,  0.81632653,\n",
       "        1.02040816,  1.2244898 ,  1.42857143,  1.63265306,  1.83673469,\n",
       "        2.04081633,  2.24489796,  2.44897959,  2.65306122,  2.85714286,\n",
       "        3.06122449,  3.26530612,  3.46938776,  3.67346939,  3.87755102,\n",
       "        4.08163265,  4.28571429,  4.48979592,  4.69387755,  4.89795918,\n",
       "        5.10204082,  5.30612245,  5.51020408,  5.71428571,  5.91836735,\n",
       "        6.12244898,  6.32653061,  6.53061224,  6.73469388,  6.93877551,\n",
       "        7.14285714,  7.34693878,  7.55102041,  7.75510204,  7.95918367,\n",
       "        8.16326531,  8.36734694,  8.57142857,  8.7755102 ,  8.97959184,\n",
       "        9.18367347,  9.3877551 ,  9.59183673,  9.79591837, 10.        ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,10,50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## eye\n",
    "\n",
    "Cria uma matriz identidade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random \n",
    "\n",
    "Numpy também tem muitas maneiras de criar arrays de números aleatórios:\n",
    "\n",
    "### rand\n",
    "Cria uma matriz da forma dada e preencha com amostras aleatórias de uma distribuição uniforme sobre ``[0, 1)``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.80118853, 0.88804014])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.30066588, 0.10177533],\n",
       "       [0.24252217, 0.59755559],\n",
       "       [0.42793395, 0.51681151],\n",
       "       [0.60907506, 0.61543069],\n",
       "       [0.4698206 , 0.1337866 ]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(5,2)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### randn\n",
    "\n",
    "Retorna uma amostra (ou amostras) da distribuição \"normal\". Ao contrário de rand, que é uniforme:\n",
    "https://pt.wikipedia.org/wiki/Distribui%C3%A7%C3%A3o_normal\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.53517564, -0.40818286])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.88625424, -1.0401899 , -0.07638597,  0.93595258,  0.79478965],\n",
       "       [ 0.53838148,  0.05206581,  0.29525211, -0.65503308,  1.91741119],\n",
       "       [-0.12000681,  1.10367852, -1.07374416, -1.15594003, -0.59816733],\n",
       "       [ 0.41647539, -0.68992829,  0.06230588,  0.31028851, -0.50594959],\n",
       "       [ 0.45325722,  1.50343113,  2.34278328,  0.34406269, -1.65868126]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(5,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### randint\n",
    "Retorna inteiros aleatórios de \"low\" (inclusive) para \"high\" (exclusivo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.random.randint(low, high)\n",
    "np.random.randint(1,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([96, 57, 25, 12,  2, 57, 45, 91,  5,  3])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,100,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Atributos de Array e Métodos\n",
    "\n",
    "Vamos discutir alguns atributos e métodos úteis ou uma matriz:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(25)\n",
    "ranarr = np.random.randint(0,50,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13, 39, 41, 14, 29, 29, 43, 16, 44, 44])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshape\n",
    "Retorna uma matriz contendo os mesmos dados com uma nova forma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(5,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 5)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(5,5).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### max,min,argmax,argmin\n",
    "\n",
    "Estes são métodos úteis para encontrar valores máximos ou mínimos, ou para encontrar seus locais de índice usando argmin ou argmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13, 39, 41, 14, 29, 29, 43, 16, 44, 44])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr.argmin()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shape\n",
    "\n",
    "Shape é um atributo que os arrays têm (não um método):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25,)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vector\n",
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
       "        16, 17, 18, 19, 20, 21, 22, 23, 24]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(1,25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 25)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(1,25).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0],\n",
       "       [ 1],\n",
       "       [ 2],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 5],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 8],\n",
       "       [ 9],\n",
       "       [10],\n",
       "       [11],\n",
       "       [12],\n",
       "       [13],\n",
       "       [14],\n",
       "       [15],\n",
       "       [16],\n",
       "       [17],\n",
       "       [18],\n",
       "       [19],\n",
       "       [20],\n",
       "       [21],\n",
       "       [22],\n",
       "       [23],\n",
       "       [24]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(25,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 1)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(25,1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dtype\n",
    "Você também pode pegar o tipo de dados do objeto na matriz:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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