{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python: Diferenciais\n",
    "\n",
    "#### Tipagem Dinâmica e Sintaxe Elegange\n",
    "\n",
    "Em C:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#include <stdio.h>\n",
    "\n",
    "int soma(int a, int b)\n",
    "{\n",
    "    return a + b;\n",
    "}\n",
    "\n",
    "void main() \n",
    "{\n",
    "    printf(\"Resultado: %d\\n\", soma(102, 459));\n",
    "    return;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Em Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultado: 561\n"
     ]
    }
   ],
   "source": [
    "def soma(a, b):\n",
    "    return a + b\n",
    "\n",
    "print(\"Resultado: %d\" % soma(102, 459))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baterias Incluídas\n",
    "\n",
    "+ Manipulação de Strings\n",
    "+ Tipos de Dados: *datetime*, *sets*, *Queue*, *Fraction*\n",
    "+ Algoritmos: *sort*, *bisect*, *random* \n",
    "+ Sistema de Arquivos (multiplataforma):\n",
    "    + cópia e manipulação de arquivos\n",
    "    + compactação de dados\n",
    "    + arquvios temporários\n",
    "    + váriaveis de ambiente\n",
    "+ Formato de Dados: *csv*, *xml*, *json*\n",
    "+ Rede: e-mail, FTP, HTML parser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Vamos Começar\n",
    "\n",
    "Baixar e Instalar o [Anaconda](https://www.continuum.io/downloads) (Distribuição Python)\n",
    "\n",
    "Inicializar o Spyder (IDE Python)\n",
    "\n",
    "![](files/resource/spyder.png)\n",
    "\n",
    "1. Editor de arquivos\n",
    "1. Inspetor de Variáveis\n",
    "1. Console Python\n",
    "\n",
    "**Atalhos Importantes:**\n",
    "\n",
    "+ Ctrl+Espaço - Lista de Funções\n",
    "+ Ctrl+I - Mostra Documentação\n",
    "+ Ctrl+Shift+I - Foco no Console\n",
    "+ Ctrl+Shift+V - Foco Inspetor de Variáveis\n",
    "+ Ctrl+Shift+E - Foco no Editor\n",
    "+ F5 - Executa Script\n",
    "\n",
    "### Console pode ser usado como Calculadora\n",
    "\n",
    "Você pode testar comandos na janela de Console, os resultados são apresentados instantaneamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.25"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "12 * 3 + 64 - 3 / 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.25\n"
     ]
    }
   ],
   "source": [
    "x = 2.5\n",
    "y = x ** 2\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Texto normal'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Texto normal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Teste de \"aspas\"'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Teste de \"aspas\"'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'texto grande assim com várias linhas'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"texto grande assim \\\n",
    "com várias linhas\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'texto grande assim com várias linhas'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"texto grande assim \" + \\\n",
    "\"com várias linhas\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formatação pode ser feita de duas maneiras:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.5 ao quadrado é 6.2'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"%.1f ao quadrado é %.1f\" % (x, y)   #old style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.5 ao quadrado é 6.2'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"{:.1f} ao quadrado é {:.1f}\".format(x, y)  #new style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais informações na [documentação oficial](https://docs.python.org/3.4/library/string.html#format-specification-mini-language)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entrada/Saída de Arquivos\n",
    "\n",
    "Escrevendo arquivo texto:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "texto_filename = \"meu-texto.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open(texto_filename, \"w\") as texto_file:\n",
    "    texto_file.write(\"Com Python\\n\")\n",
    "    texto_file.write(\"sou mais feliz\\n\")\n",
    "    texto_file.write(\"-- fim --\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lendo arquivo texto:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Com Python\\n'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(texto_filename, \"r\") as texto_file:\n",
    "    texto = texto_file.readline()\n",
    "texto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Com Python\n",
      "sou mais feliz\n",
      "-- fim --\n",
      "\n"
     ]
    }
   ],
   "source": [
    "texto_lido = \"\"\n",
    "with open(texto_filename, \"r\") as texto_file:\n",
    "    for line in texto_file:\n",
    "        texto_lido += line\n",
    "print(texto_lido)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forma insegura de abrir um arquivo"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "texto_file = open(texto_filename)\n",
    "texto_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais informações na [documentação](https://docs.python.org/3.4/library/os.html#module-os)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Listas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Botafogo'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clubes = [\"Botafogo\", \"Criciuma\", \"Náutico\", \"Vitória\"]\n",
    "clubes[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Atenção:** Indexação de vetores no Python inicia em 0 (zero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['maça', 1, 15.3, 11]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Listas podem ter diferentes tipos (porém, não é boa prática)\n",
    "[\"maça\", 1, 15.3, 0xB]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Botafogo', 'Criciuma', 'Náutico', 'Vitória', 'Bragantino']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clubes.append(\"Bragantino\")\n",
    "clubes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bahia', 'Botafogo', 'Criciuma', 'Náutico', 'Vitória', 'Bragantino']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clubes.insert(0, \"Bahia\")\n",
    "clubes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bahia'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clube = clubes.pop(0)\n",
    "clube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Indexação/Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bragantino'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clubes[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Náutico', 'Vitória', 'Bragantino']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clubes[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Criciuma', 'Náutico', 'Vitória']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clubes[1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dicionários"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fruits:  dict_keys(['banana', 'orange', 'apple'])\n"
     ]
    }
   ],
   "source": [
    "# Dicts\n",
    "fruit_calories = {}\n",
    "fruit_calories[\"apple\"] = 53\n",
    "fruit_calories[\"banana\"] = 95\n",
    "fruit_calories[\"orange\"] = 63\n",
    "\n",
    "# Getting dict keys\n",
    "print(\"Fruits: \", fruit_calories.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calories:  dict_values([95, 63, 53])\n"
     ]
    }
   ],
   "source": [
    "# Getting dict values\n",
    "print(\"Calories: \", fruit_calories.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Orange calories: 63\n"
     ]
    }
   ],
   "source": [
    "# Query\n",
    "print(\"Orange calories:\", fruit_calories[\"orange\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Orange has 63 calories\n"
     ]
    }
   ],
   "source": [
    "# Formated response\n",
    "print(\"Orange has {0} calories\".format(fruit_calories[\"orange\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Condicionais/Repetição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Texto Pequeno\n"
     ]
    }
   ],
   "source": [
    "if len(texto_lido) > 100:\n",
    "    print(\"Texto Grande\")\n",
    "else:\n",
    "    print(\"Texto Pequeno\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Texto Médio\n"
     ]
    }
   ],
   "source": [
    "if len(texto_lido) > 100:\n",
    "    print(\"Texto Grande\")\n",
    "elif len(texto_lido) > 20:\n",
    "    print(\"Texto Médio\")\n",
    "else:\n",
    "    print(\"Texto Pequeno\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Botafogo\n",
      "Criciuma\n",
      "Náutico\n",
      "Vitória\n",
      "Bragantino\n"
     ]
    }
   ],
   "source": [
    "for clube in clubes:\n",
    "    print(clube)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 4):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Botafogo\n",
      "2 Criciuma\n",
      "3 Náutico\n",
      "4 Vitória\n",
      "5 Bragantino\n"
     ]
    }
   ],
   "source": [
    "for i, clube in enumerate(clubes):\n",
    "    print (i+1, clube)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Botafogo\n",
      "Bragantino\n",
      "Criciuma\n",
      "Náutico\n",
      "Vitória\n"
     ]
    }
   ],
   "source": [
    "for clube in sorted(clubes):\n",
    "    print(clube)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0           1.00    0.00    1.00\n",
      "2           4.00    0.91   -0.42\n",
      "4          16.00   -0.76   -0.65\n",
      "6          64.00   -0.28    0.96\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "# De 0 a 10, imprime somente números pares e interromple se x >= 8\n",
    "for x in range(10):\n",
    "    if x % 2 != 0:\n",
    "        continue # pula para próxima iteração (x+1)\n",
    "    if x >= 8:\n",
    "        break # finaliza o loop\n",
    "    print(\"{:<8d}{:8.2f}{: 8.2f}{: 8.2f}\".format(x, math.pow(2,x), math.sin(x), math.cos(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 1024)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 2\n",
    "i = 0\n",
    "while i < 10 and x < 1000:\n",
    "    i += 1\n",
    "    x = x * 2\n",
    "i, x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funções"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def count_char(word, char):\n",
    "    count = 0\n",
    "    for w in word:\n",
    "        if w == char:\n",
    "            count += 1\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_char(\"ESSS\", \"S\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Argumentos Default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def count_char(text, char, case_sens=False, stop_eol=False):\n",
    "    count = 0\n",
    "    if stop_eol:\n",
    "        text = text.split(\"\\n\")[0]\n",
    "    if not case_sens:\n",
    "        text = text.lower()\n",
    "        char = char.lower()\n",
    "    for w in text:\n",
    "        if w == char:\n",
    "            count += 1\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_char(\"ESSS\", \"s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_char(\"ESSS\", \"s\", True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Argumento Nomeado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Melhor\n",
    "count_char(\"ESSS\", \"s\", case_sens=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_char(texto_lido, \"m\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_char(texto_lido, \"m\", stop_eol=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bibliotecas\n",
    "\n",
    "#### math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999996829318346"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.sin(3.14 / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.sin(math.pi / 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais informações na [documentação](https://docs.python.org/3.4/library/math.html?highlight=math#module-math)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\itghisi'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_dir = os.path.expanduser(\"~\")\n",
    "home_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X:\\\\py4eng-course\\\\notebooks\\\\webinar-2015'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.isdir(r\"C:\\\\Windows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais informações na [documentação](https://docs.python.org/3.4/library/os.html#module-os)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criação de Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.2,  10. ,  33. ,  12.4,   5. ])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.array([1.2, 10, 33, 12.4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10, \"d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 6, 6, 3, 8, 1, 8, 3, 2, 8])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.random.randint(1, 10, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r = np.linspace(0, np.pi/2, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais informacões no [manual da NumPy](http://docs.scipy.org/doc/numpy/reference/routines.array-creation.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Operações Básicas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False,  True,  True,  True,  True,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v =  np.sin(r)\n",
    "v > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,   1.73648178,   3.42020143,   5.        ,\n",
       "         6.4278761 ,   7.66044443,   8.66025404,   9.39692621,\n",
       "         9.84807753,  10.        ])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.01736482,  0.03420201,  0.05      ,  0.06427876,\n",
       "        0.07660444,  0.08660254,  0.09396926,  0.09848078,  0.1       ])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v / 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "A = np.array([[3, 12, 4],\n",
    "              [0,  3, 9],\n",
    "              [1,  4, 3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.array([3, 4, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([73, 48, 31])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(A, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais informacões [aqui](http://docs.scipy.org/doc/numpy/user/basics.indexing.html#other-indexing-options) e [aqui](http://docs.scipy.org/doc/numpy/reference/routines.array-creation.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Manipulação de Arrays (slicing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.17364818,  0.34202014,  0.5       ,  0.64278761])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.93969262,  0.98480775,  1.        ])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  4,  4, 73, 48, 31])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([x, np.dot(A, x)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais informacões no [manual da NumPy](http://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7967320>]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x79dbe10>]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E/5InZv/JJieR959a5EyELH7utd6HdBGTiEgR0n2QRESKkMpdRKQIqdxFRIqQ\nyl1EpAip3EVEipDKXUSkCKncRUSKkMpdRKQI/T+T4HhDsieIUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7967d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(r, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x80c7f28>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5X/cidfT6jmFIUod6dqZ3P7EwJKlzvRz0liSdhCwMSVKRWgojIrZFxAMR8WBEXHmc+X4j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\n0QgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x79f8b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(r, v, \"ro\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"$sin(x)$\")\n",
    "plt.title(\"Tutorial do Plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mais Informações:\n",
    "\n",
    "+ [PyPloy Tutorial](http://matplotlib.org/users/pyplot_tutorial.html)\n",
    "+ [`plot()` function reference](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot)\n",
    "+ [Samples gallery](http://matplotlib.org/gallery.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy.integrate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999999999999"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fx, err  = scipy.integrate.quad(math.cos, 0, math.pi/2.0)\n",
    "fx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999999999999"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.integrate import quad\n",
    "fx, err = quad(math.cos, 0, math.pi/2.0)\n",
    "fx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}