Modulos do Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3b749315",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ccb8bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77afc4bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1) Exemplo de gráfico.\n",
    "\n",
    "x = [1,2,3,4,5,6,7]\n",
    "y = [1,4,9,16,25,36,49]\n",
    "\n",
    "plt.plot(x,y,color='green') #Plota o gráfico\n",
    "plt.scatter(x,y, color='red') #Plota os pontos do gráfico\n",
    "plt.title('Valores e seus quadrados') #Título ao gráfico\n",
    "plt.show() #Mostra o gráfico"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfc689b7",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "---\n",
    "# Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91ab7930",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e74ac2a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    data  valor produto  qnt\n",
      "0  15/11    200  feijão    5\n",
      "1  15/12    300   arroz   10\n"
     ]
    },
    {
     "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>data</th>\n",
       "      <th>valor</th>\n",
       "      <th>produto</th>\n",
       "      <th>qnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15/11</td>\n",
       "      <td>200</td>\n",
       "      <td>feijão</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15/12</td>\n",
       "      <td>300</td>\n",
       "      <td>arroz</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data  valor produto  qnt\n",
       "0  15/11    200  feijão    5\n",
       "1  15/12    300   arroz   10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1) Exemplo de tabela.\n",
    "\n",
    "venda = {'data':['15/11', '15/12'],\n",
    "         'valor': [200,300],\n",
    "         'produto': ['feijão', 'arroz'],\n",
    "         'qnt': [5,10],\n",
    "        }                                         \n",
    "\n",
    "vendas_df = pd.DataFrame(venda)\n",
    "print(vendas_df) #printa a tabela normal\n",
    "display(vendas_df) #printa a tabela estiliazada"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c07165",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "---\n",
    "# Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf163ab",
   "metadata": {},
   "source": [
    "# Arrays\n",
    "    Essa estrutura é parecido com listas, entretanto possui algumas diferenças. No primeiro box temos as arrays unidimensionais; no segundo, as arrays bidimensionais; no terceiro, temos algumas operaçãoes com arrays; no quarto, arrays tridimensionais "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9147c582",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiplicação de listas l1:\n",
      " [0, 2, 4, 6, 8, 10, 12, 14, 0, 2, 4, 6, 8, 10, 12, 14] \n",
      "\n",
      "Multiplicação de termos de listas l2:\n",
      " [0, 4, 8, 12, 16, 20, 24, 28] \n",
      "\n",
      "Para saber o tipo de arrays:\n",
      " int64 \n",
      "\n",
      "Para modificar o tipo de arrays:\n",
      " int16 \n",
      "\n",
      "Multiplicação de arrays:\n",
      " [ 0  4  8 12 16 20 24 28] \n",
      "\n",
      "Primeiro exemplo de fatiamento de n_f:\n",
      " 20\n",
      "Primeiro exemplo de fatiamento de n_f:\n",
      " [ 4  6  8 10 12 14 16 18] \n",
      "\n",
      "Modificação de uma array de n_m:\n",
      " [ 0  4  6  8 10 12 14 16]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "                                            ###Arrays unidimensionais\n",
    "\n",
    "# Multiplicação de listas: (Ao fazer a multiplicação da lista, temos uma lista duplicada\n",
    "l1 = [0,2,4,6,8,10,12,14]\n",
    "print('Multiplicação de listas l1:\\n', l1*2, '\\n')\n",
    "\n",
    "    # Multiplicação de cada termo de uma lista: (Para multiplciar cada termo é necessário fazer o seguinte:\n",
    "l2 = [2*x for x in l1]\n",
    "print('Multiplicação de termos de listas l2:\\n', l2, '\\n')\n",
    "\n",
    "# Criação de arrays: (Basta colocar uma lista dentro dos parênteses pertencente a array)\n",
    "n = np.array([1,2,3,4,5,6,7,8,9])\n",
    "\n",
    "    # Tipos de Arrays: (Basta utilizar 'var.dtype' sendo que por padrão vem 'int64')\n",
    "print('Para saber o tipo de arrays:\\n', n.dtype, '\\n')\n",
    "\n",
    "        # Modificar o tipo dos elementos de uma array: (Na criação do array, basta colocar uma virgular e especificar) \n",
    "n_t = np.array([1,2,3,4,5,6,7,8,9], np.int16)\n",
    "print('Para modificar o tipo de arrays:\\n', n_t.dtype, '\\n')\n",
    "\n",
    "# Utilizando array para multiplicar cada termo:\n",
    "a = np.array(l1)\n",
    "b = 2*a\n",
    "print('Multiplicação de arrays:\\n', b, '\\n')\n",
    "\n",
    "# É possível fazer o fatiamento da array normalmente:\n",
    "n_f = np.array([2,4,6,8,10,12,14,16,18,20])\n",
    "print('Primeiro exemplo de fatiamento de n_f:\\n', n_f[-1])\n",
    "print('Primeiro exemplo de fatiamento de n_f:\\n', n_f[1:9], '\\n') \n",
    "\n",
    "# É possível fazer atribuições e modificações no array:\n",
    "n_m = np.array([2,4,6,8,10,12,14,16])\n",
    "n_m[0] = 0\n",
    "print('Modificação de uma array de n_m:\\n', n_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7366262d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exemplo de array bidimensional a:\n",
      " [[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]] \n",
      "\n",
      "Acessar os elementos de b:\n",
      " 2 \n",
      "\n",
      "Alterar os elementos de b:\n",
      " [[1 8 3]\n",
      " [4 5 6]\n",
      " [7 8 9]] \n",
      "\n",
      "Primeira coluna: [1 4 7]\n",
      "Segunda coluna: [2 5 8]\n",
      "Terceira coluna: [3 6 9] \n",
      "\n",
      "Primeira linha: [1 2 3]\n",
      "Segunda linha: [4 5 6]\n",
      "Terceira linha: [7 8 9]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "                                    ###Arrays bidimensionais\n",
    "# Criação de arrays bidemensionais: (Cada lista será linha e cada elemento pertence a uma coluna)\n",
    "a = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
    "print('Exemplo de array bidimensional a:\\n', a, '\\n')\n",
    "\n",
    "    # Para acessar os elementos da array de duas dimenções: ([linha,coluna]); Lembre-se que começando do [0,0]\n",
    "b = a.copy()\n",
    "print('Acessar os elementos de b:\\n', b[0,1], '\\n')\n",
    "\n",
    "    # Alterar elementos:\n",
    "b[0,1] = 8\n",
    "print('Alterar os elementos de b:\\n', b, '\\n')\n",
    "\n",
    "# Visualizar apenas uma coluna inteira: (Coluna: dentro do slice [os dois pontos == coluna, número da coluna])\n",
    "print('Primeira coluna:', a[:,0])\n",
    "print('Segunda coluna:', a[:,1])\n",
    "print('Terceira coluna:', a[:,2], '\\n')\n",
    "\n",
    "# Visualizar apenas uma linha inteira:\n",
    "print('Primeira linha:', a[0,:])\n",
    "print('Segunda linha:', a[1,:])\n",
    "print('Terceira linha:', a[2,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2a95b563",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de dimensões de a1:\n",
      " 1\n",
      "Número de dimensões de a2:\n",
      " 2 \n",
      "\n",
      "Tamanho de a1:(Número de elementos da linha)\n",
      " (10,)\n",
      "Tamanho de a2:(Número de elementos dalina, Números de elementos da coluna)\n",
      " (3, 3) \n",
      "\n",
      "Número de elementos de a1:\n",
      " 10\n",
      "Número de elementos de a2:\n",
      " 9\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Número de dimensções de uma array: Usar 'var.ndim'\n",
    "\n",
    "a1 = np.array([1,2,3,4,5,6,7,8,9,10])\n",
    "a2 = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
    "\n",
    "print('Número de dimensões de a1:\\n', a1.ndim) \n",
    "print('Número de dimensões de a2:\\n', a2.ndim, '\\n') \n",
    "\n",
    "# Tamanho do array: (Usar o var.shape)\n",
    "print('Tamanho de a1:(Número de elementos da linha)\\n', a1.shape)\n",
    "print('Tamanho de a2:(Número de elementos dalina, Números de elementos da coluna)\\n', a2.shape, '\\n')\n",
    "\n",
    "# Número de elementos: (Usar var.size)\n",
    "print('Número de elementos de a1:\\n', a1.size) \n",
    "print('Número de elementos de a2:\\n', a2.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b5077112",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array tridimensional: \n",
      " [[[ 1  2  3]\n",
      "  [ 4  5  6]\n",
      "  [ 7  8  9]]\n",
      "\n",
      " [[10 11 12]\n",
      "  [13 14 15]\n",
      "  [16 17 18]]\n",
      "\n",
      " [[19 20 21]\n",
      "  [22 23 24]\n",
      "  [25 26 27]]] \n",
      "\n",
      "Para o elemento correspondente a esse índece: \n",
      " 10 \n",
      "\n",
      "Para o elemento correspondente a esse índece: \n",
      " 4 \n",
      "\n",
      "Para o elemento correspondente a esse índece: \n",
      " 2 \n",
      "\n",
      "Para visualizar apenas um bloco: \n",
      " [[10 11 12]\n",
      " [13 14 15]\n",
      " [16 17 18]] \n",
      "\n",
      "Para visualizar apenas um linha: \n",
      " [[ 4  5  6]\n",
      " [13 14 15]\n",
      " [22 23 24]] \n",
      "\n",
      "Para visualizar apenas um coluna: \n",
      " [[ 2  5  8]\n",
      " [11 14 17]\n",
      " [20 23 26]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "                                        #Arrays tridimensionais\n",
    "# Criar arrays de três dimensões:\n",
    "a3 = np.array([[[1,2,3],[4,5,6],[7,8,9]],[[10,11,12],[13,14,15],[16,17,18]],[[19,20,21],[22,23,24],[25,26,27]]])\n",
    "print('Array tridimensional: \\n',a3, '\\n')\n",
    "\n",
    "# Visualizar elementos específicos de uma array tridimensional: ***([bloco, linha, coluna])\n",
    "print('Para o elemento correspondente a esse índece: \\n', a3[1,0,0], '\\n')\n",
    "print('Para o elemento correspondente a esse índece: \\n', a3[0,1,0], '\\n')\n",
    "print('Para o elemento correspondente a esse índece: \\n', a3[0,0,1], '\\n')\n",
    "\n",
    "# Visualizar apenas um bloco, linha ou colina de uma array tridimensional:\n",
    "print('Para visualizar apenas um bloco: \\n', a3[1,:,:], '\\n')\n",
    "print('Para visualizar apenas um linha: \\n', a3[:,1,:], '\\n')\n",
    "print('Para visualizar apenas um coluna: \\n', a3[:,:,1], '\\n')\n",
    "\n",
    "# Número de dimensões do array"
   ]
  }
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