indexacao_slicing_numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexação e Slicing com Numpy\n",
    "### Uma vez que haja um vetor ou uma matriz, podemos buscar apenas algumas partes desses dados.\n",
    "### Buscar apenas algumas \"células da planilha\": slicing|fatiamento\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando o módulo Numpy\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "array1 = np.arange(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Primeiro elemento do array (em Python a indexação começa por 0)\n",
    "array1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Segundo elemento do array (em Python a indexação começa por 0)\n",
    "array1[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Último elemento do array (em Python a indexação começa por 0)\n",
    "array1[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando um array\n",
    "array2 = np.arange(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 2, 1, 0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Invertendo a estrutura do array\n",
    "array2[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Trabalhando com Array multi-dimensional "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('Numpy.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "array3 = np.array([[190, 290, 490], [129, 458, 567], [761, 902, 346]]) #listas de listas [[]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[190, 290, 490],\n",
       "       [129, 458, 567],\n",
       "       [761, 902, 346]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "902"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing da Matriz - retornar elemento da terceira linha, segunda coluna\n",
    "array3[2, 1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Atribuindo um valor a um elemento da Matriz na segunda linha, segunda coluna\n",
    "array3[1, 1] = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[190, 290, 490],\n",
       "       [129,  10, 567],\n",
       "       [761, 902, 346]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([761, 902, 346])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retornando a terceira linha da Matriz\n",
    "array3[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([761, 902, 346])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retornando a terceira linha da Matriz - notação com vírgula\n",
    "array3[2,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([290,  10, 902])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retornando todas as linhas da segunda coluna da Matriz\n",
    "array3[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "array4 = np.array([100, 200, 300])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 5 is out of bounds for axis 0 with size 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-305837fed46b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marray4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: index 5 is out of bounds for axis 0 with size 3"
     ]
    }
   ],
   "source": [
    "array4[5] # índice 5 está fora dos limites do eixo 0 de tamanho 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<h2>Slicing de Arrays</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "array5 = np.arange(50) * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32,\n",
       "       34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66,\n",
       "       68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 14, 18])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array5[5:10:2] #quero retornar elementos dos array que comecem com índice 5 (valor 10), até o índice 10 (valor 20 e exclusivo) e step 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retorna todos os valores até o elemento de index 4 (quinto elemento exclusive)\n",
    "array5[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40,\n",
       "       42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74,\n",
       "       76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A partir do elemento de index 4, retorna todos os valores (contrário ao anterior)\n",
    "array5[4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array5[::3] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32,\n",
       "       34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66,\n",
       "       68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# O slicing não altera o objeto original\n",
    "array5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32,\n",
       "       34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66,\n",
       "       68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Podemos usar as técnicas de slicing para alterar elementos ou conjuntos de elementos dentro do array\n",
    "# buscar tudo até o índice 3 exclusivo e atribuir valor específico a estes índices\n",
    "array5[:3] = 1; array5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1,  1,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32,\n",
       "       34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66,\n",
       "       68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Atribuindo um array a uma parte de outro array. Substituir tais índices por um array de 1, np.ones. \n",
    "array5[:4] = np.ones(4); array5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Criando um subset de um array</h2>\n",
    "<h3>Aplicando slicing, o objeto é buscar um subset (subconjunto) do conjunto de dados</h3>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "array7 = np.arange(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "array8 = array7[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copiando um array com np.copy\n",
    "<h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "array9 = np.arange(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "array10 = array9.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array10"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}