{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manipulando Listas como array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista=[[0, 101, 102, 103, 104],\n",
    " [1, 5, 6, 7, 8],\n",
    " [2, 9, 10, 11, 12],\n",
    " [3, 13, 14, 15, 16],\n",
    " [4, 17, 18, 19, 20],\n",
    " [5,21,22,23,24]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 101, 102, 103, 104],\n",
       " [1, 5, 6, 7, 8],\n",
       " [2, 9, 10, 11, 12],\n",
       " [3, 13, 14, 15, 16],\n",
       " [4, 17, 18, 19, 20],\n",
       " [5, 21, 22, 23, 24]]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "aLista=np.array(lista)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0, 101, 102, 103, 104],\n",
       "       [  1,   5,   6,   7,   8],\n",
       "       [  2,   9,  10,  11,  12],\n",
       "       [  3,  13,  14,  15,  16],\n",
       "       [  4,  17,  18,  19,  20],\n",
       "       [  5,  21,  22,  23,  24]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aLista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "aCol0=list(aLista[:,0].copy())  #tomando somente os primeiro elementos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aCol0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aLista.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aLista.shape  #formato em linhas e colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "vetLinha=np.array([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vetLinha.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "vetCol=np.array([[1],[2],[3],[4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vetCol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vetCol.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### O objeto matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "matLinha=np.matrix(vetLinha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1, 2, 3, 4]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matLinha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 4)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matLinha.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "matCol=matLinha.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1],\n",
       "        [2],\n",
       "        [3],\n",
       "        [4]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matCol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matCol.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "matCol=np.matrix([[1],[2],[3],[4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1],\n",
       "        [2],\n",
       "        [3],\n",
       "        [4]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matCol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matCol.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "linha, coluna=matCol.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linha, coluna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lembrando o objeto set (conjunto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "conjunto = set([1,4, 8, 20, 7, 40, 8, 50, 40, 'banana', 'abacaxi', 'laranja', 'banana'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 20, 4, 40, 50, 7, 8, 'abacaxi', 'banana', 'laranja'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conjunto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "listaConjunto=list(conjunto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 'laranja', 4, 7, 8, 40, 'banana', 50, 20, 'abacaxi']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listaConjunto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listaConjunto.index('banana')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### O que são módulos e pacotes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image  #importando o pacote Ipython que tem o modulo display que tem a função Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"Módulos e Pacotes em Python.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grosseiramente, pacotes são pastas com um arquivo __init__.py vazio e outros arquivos .py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "s=\"Maria\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MARIA'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Maria'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getnewargs__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__mod__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'capitalize',\n",
       " 'casefold',\n",
       " 'center',\n",
       " 'count',\n",
       " 'encode',\n",
       " 'endswith',\n",
       " 'expandtabs',\n",
       " 'find',\n",
       " 'format',\n",
       " 'format_map',\n",
       " 'index',\n",
       " 'isalnum',\n",
       " 'isalpha',\n",
       " 'isascii',\n",
       " 'isdecimal',\n",
       " 'isdigit',\n",
       " 'isidentifier',\n",
       " 'islower',\n",
       " 'isnumeric',\n",
       " 'isprintable',\n",
       " 'isspace',\n",
       " 'istitle',\n",
       " 'isupper',\n",
       " 'join',\n",
       " 'ljust',\n",
       " 'lower',\n",
       " 'lstrip',\n",
       " 'maketrans',\n",
       " 'partition',\n",
       " 'removeprefix',\n",
       " 'removesuffix',\n",
       " 'replace',\n",
       " 'rfind',\n",
       " 'rindex',\n",
       " 'rjust',\n",
       " 'rpartition',\n",
       " 'rsplit',\n",
       " 'rstrip',\n",
       " 'split',\n",
       " 'splitlines',\n",
       " 'startswith',\n",
       " 'strip',\n",
       " 'swapcase',\n",
       " 'title',\n",
       " 'translate',\n",
       " 'upper',\n",
       " 'zfill']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "st=\"João, Maria, José, Antônio Carlos\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['João,', 'Maria,', 'José,', 'Antônio', 'Carlos']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.split()   #cria uma lista de strings separando a cada espaço. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['João', ' Maria', ' José', ' Antônio Carlos']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.split(\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E se usasse outro separador?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "st=\"João; Maria; José; Antônio Carlos\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['João;', 'Maria;', 'José;', 'Antônio', 'Carlos']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Por default, split considera separador os espaços"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['João', ' Maria', ' José', ' Antônio Carlos']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.split(';') #especificando que o separador é o ';'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "s=\"maria\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Maria'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mar'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'airam'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comandos de entrada de dados, operadores, controle de fluxo, condicionais,etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entre com seu salário: 30000.00\n"
     ]
    }
   ],
   "source": [
    "Salario = input('Entre com seu salário: ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'30000.00'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30000.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(Salario)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entre com seu salário: 35000.00\n"
     ]
    }
   ],
   "source": [
    "Salario=float(input('Entre com seu salário: '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35000.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Salario   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Salario)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Entrada e saida de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anos de serviço: 25\n",
      "Valor por ano: 60000\n",
      "Bonus de 1500000.00\n"
     ]
    }
   ],
   "source": [
    "anos = int(input(\"Anos de serviço: \"))\n",
    "valorPorAno=float(input(\"Valor por ano: \"))\n",
    "bonus=anos*valorPorAno\n",
    "#print(\"Bonus de R$ %5.2f\" %(bonus)) # imprimir o valor de bonus com 5 casas e no maximo 2 decimais em ponto flutuante\n",
    "print(f\"Bonus de {bonus:5.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Usando o print com mais de uma variável"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "João tem 22 e 51.34 no bolso\n"
     ]
    }
   ],
   "source": [
    "nome = \"João\"\n",
    "idade = 22\n",
    "grana=51.34\n",
    "\n",
    "#print(\"%s tem %d anos e R$%f no bolso.\" %(nome,idade,grana))\n",
    "print(f\"{nome} tem {idade} e {grana} no bolso\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        João tem  22 anos e R$51.34 no bolso.\n"
     ]
    }
   ],
   "source": [
    "print(\"%12s tem %3d anos e R$%5.2f no bolso.\" %(nome,idade,grana))  #atenção onde começa a impressao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        João tem 022 anos e R$51.34 no bolso.\n"
     ]
    }
   ],
   "source": [
    "print(\"%12s tem %03d anos e R$%5.2f no bolso.\" %(nome,idade,grana))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "João         tem 22  anos e R$51.34 no bolso.\n"
     ]
    }
   ],
   "source": [
    "print(\"%-12s tem %-3d anos e R$%-5.2f no bolso.\" %(nome,idade,grana)) #observe o sinal -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operadores relacionais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=10\n",
    "b=5\n",
    "c=10\n",
    "d=8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a<b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a>b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a==c   #a é igual a c?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a!=c   #a é diferente de c?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a != b "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b<=d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c>=8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b<d<a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a==c and c<d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a==c>d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a==c or c>d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not (a!=b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Condicionais if, elif, else"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a é maior que b\n",
      "\n",
      "Eu te disse que o a é maior\n"
     ]
    }
   ],
   "source": [
    "if a>b:\n",
    "    print(\"a é maior que b\\n\")\n",
    "    print(\"Eu te disse que o a é maior\")\n",
    "else:\n",
    "    print(\"b é maior que a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b é menor que d\n",
      "\n",
      " eu disse que era menor\n"
     ]
    }
   ],
   "source": [
    "if a>b:\n",
    "    if b>d:\n",
    "        print(\"b maior que d\")\n",
    "        print(\"\\nas vezes acontece\")\n",
    "    else:\n",
    "        print(\"b é menor que d\")\n",
    "        print(\"\\n eu disse que era menor\")\n",
    "    #endelse\n",
    "else:\n",
    "    print(\"a é menor do que b\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Digite a categoria do produto: 5\n",
      "O preço do produto é: R$ 31.00\n"
     ]
    }
   ],
   "source": [
    "categoria=int(input(\"Digite a categoria do produto: \"))\n",
    "if categoria==1:\n",
    "    preço=10\n",
    "elif categoria==2:\n",
    "    preço=18\n",
    "elif categoria==3:\n",
    "    preço=23\n",
    "elif categoria==4:\n",
    "    preço=26\n",
    "elif categoria==5:\n",
    "    preço=31\n",
    "else:\n",
    "    print(\"Categoria inválida, digite um valor entre 1 e 5\")\n",
    "    preço=0\n",
    "print(\"O preço do produto é: R$%6.2f\" %preço)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repetição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "x=1\n",
    "while x<=3:\n",
    "    print(x)\n",
    "    x=x+1 # ou x += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Digite um número a somar ou 0 para sair:2\n",
      "Digite um número a somar ou 0 para sair:3\n",
      "Digite um número a somar ou 0 para sair:4\n",
      "Digite um número a somar ou 0 para sair:0\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "s=0\n",
    "while True:\n",
    "    v=int(input(\"Digite um número a somar ou 0 para sair:\"))\n",
    "    if v==0:\n",
    "        break\n",
    "    s += v\n",
    "print(s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista=[0,40, 22, 33]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0 in lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 in lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "r=list(range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "r=list(range(2,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Como é bom programar em python\n",
      "\n",
      "Como é bom programar em python\n",
      "\n",
      "Como é bom programar em python\n",
      "\n",
      "Como é bom programar em python\n",
      "\n",
      "Como é bom programar em python\n",
      "\n",
      "Como é bom programar em python\n",
      "\n",
      "Como é bom programar em python\n",
      "\n",
      "Como é bom programar em python\n",
      "\n",
      "Como é bom programar em python\n",
      "\n",
      "Como é bom programar em python\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for r in range(10):                           #para cada r em [0,1, ..,9]\n",
    "    print(\"Como é bom programar em python\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista=['a', 'b','c', 'd', \"e\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<enumerate at 0x7f1280c500c0>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enumerate(lista)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0, 'a'), (1, 'b'), (2, 'c'), (3, 'd'), (4, 'e'))"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple(enumerate(lista))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: a\n",
      "1: b\n",
      "2: c\n",
      "3: d\n",
      "4: e\n"
     ]
    }
   ],
   "source": [
    "for index, elemento in enumerate(lista):\n",
    "    print(\"%s: %s\" %(index,elemento))\n",
    "    #print(f\"{index}: {elemento}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerador = int(input(\"Numerador: \"))\n",
    "denominador = int(input(\"Denominador: \"))\n",
    "try:\n",
    "    quociente=numerador/denominador\n",
    "except ZeroDivisionError:\n",
    "    print(\"Divisao por zero. Denominador não pode ser nulo\")\n",
    "finally:\n",
    "    print(\"Exemplo de try/except terminado\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerador = int(input(\"Numerador: \"))\n",
    "denominador = int(input(\"Denominador: \"))\n",
    "try:\n",
    "    quociente=numerador/denominador\n",
    "except ZeroDivisionError:\n",
    "    print(\"Divisao por zero. Denominador não pode ser nulo\")\n",
    "finally:\n",
    "    print(\"Exemplo de try/except terminado\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "produto={\"sapato\": 90.50,\"banana\":5.00, \"camisa\": 95, \"goiaba\": 1.3, \"aquecedor\": 179.90} #dicionario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('sapato', 90.5), ('banana', 5.0), ('camisa', 95), ('goiaba', 1.3), ('aquecedor', 179.9)])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "produto.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "listNomeProd=list(produto.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sapato', 'banana', 'camisa', 'goiaba', 'aquecedor']"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listNomeProd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "listNomeProd.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['aquecedor', 'banana', 'camisa', 'goiaba', 'sapato']"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listNomeProd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[[NomeProduto,produto[NomeProduto]] for NomeProduto in listNomeProd]\n",
    "#lista=[]\n",
    "#for NomeProduto in listNomeProd:\n",
    "#    lista.append([NomeProduto,produto[NomeProduto]])\n",
    "#lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "turmaA=[\"João\", \"Maria\", \"José\"]\n",
    "notaTurmaA=[5.0, 2.8, 6.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "notaTurma=list(zip(turmaA,notaTurmaA))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('João', 5.0), ('Maria', 2.8), ('José', 6.2)]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notaTurma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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