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   "source": [
    "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 6</font>\n",
    "\n",
    "## Download: http://github.com/dsacademybr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update e Delete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import random\n",
    "import time\n",
    "import datetime\n",
    " \n",
    "# Criando uma conexão\n",
    "conn = sqlite3.connect('dsa.db')   \n",
    "\n",
    "# Criando um cursor\n",
    "c = conn.cursor()\n",
    " \n",
    "# Função para criar uma tabela\n",
    "def create_table():\n",
    "    c.execute('CREATE TABLE IF NOT EXISTS produtos(id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, date TEXT, '\\\n",
    "              'prod_name TEXT, valor REAL)')\n",
    "      \n",
    "# Função para inserir uma linha\n",
    "def data_insert():\n",
    "    c.execute(\"INSERT INTO produtos VALUES(002, '02-05-2016', 'teclado', 130 )\")\n",
    "    conn.commit()\n",
    "    c.close()\n",
    "    conn.close()\n",
    "    \n",
    "# Usando variáveis para inserir dados    \n",
    "def data_insert_var():\n",
    "    new_date = datetime.datetime.now()\n",
    "    new_prod_name = 'monitor'\n",
    "    new_valor = random.randrange(50,100)\n",
    "    c.execute(\"INSERT INTO produtos (date, prod_name, valor) VALUES (?, ?, ?, ?)\", \n",
    "              (new_date, new_prod_name, new_valor))\n",
    "    conn.commit()\n",
    "    \n",
    "# Leitura de dados\n",
    "def leitura_todos_dados():\n",
    "    c.execute(\"SELECT * FROM PRODUTOS\")\n",
    "    for linha in c.fetchall():\n",
    "        print(linha)\n",
    "        \n",
    "# Leitura de registros específicos\n",
    "def leitura_registros():\n",
    "    c.execute(\"SELECT * FROM PRODUTOS WHERE valor > 70.0\")\n",
    "    for linha in c.fetchall():\n",
    "        print(linha)      \n",
    "        \n",
    "# Leitura de colunas específicos\n",
    "def leitura_colunas():\n",
    "    c.execute(\"SELECT * FROM PRODUTOS\")\n",
    "    for linha in c.fetchall():\n",
    "        print(linha[3])  \n",
    "        \n",
    "# Update\n",
    "def atualiza_dados():\n",
    "    c.execute(\"UPDATE produtos SET valor = 70.00 WHERE valor = 98.0\")\n",
    "    conn.commit()\n",
    "    \n",
    "# Delete\n",
    "def remove_dados():\n",
    "    c.execute(\"DELETE FROM produtos WHERE valor = 62.0\")\n",
    "    conn.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "atualiza_dados()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, '2018-05-02 14:32:11', 'Teclado', 90.0)\n",
      "(11, '2018-04-18 15:32:15.064730', 'Monitor', 52.0)\n",
      "(12, '2018-04-18 15:32:16.074631', 'Monitor', 70.0)\n",
      "(13, '2018-04-18 15:32:17.083495', 'Monitor', 94.0)\n",
      "(14, '2018-04-18 15:32:18.092251', 'Monitor', 64.0)\n",
      "(15, '2018-04-18 15:32:19.100759', 'Monitor', 74.0)\n",
      "(16, '2018-04-18 15:32:20.112758', 'Monitor', 62.0)\n",
      "(17, '2018-04-18 15:32:21.120451', 'Monitor', 86.0)\n",
      "(18, '2018-04-18 15:32:22.130355', 'Monitor', 57.0)\n",
      "(19, '2018-04-18 15:32:23.140021', 'Monitor', 53.0)\n",
      "(20, '2018-04-18 15:32:24.150483', 'Monitor', 78.0)\n"
     ]
    }
   ],
   "source": [
    "leitura_todos_dados()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "remove_dados()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, '2018-05-02 14:32:11', 'Teclado', 90.0)\n",
      "(11, '2018-04-18 15:32:15.064730', 'Monitor', 52.0)\n",
      "(12, '2018-04-18 15:32:16.074631', 'Monitor', 70.0)\n",
      "(13, '2018-04-18 15:32:17.083495', 'Monitor', 94.0)\n",
      "(14, '2018-04-18 15:32:18.092251', 'Monitor', 64.0)\n",
      "(15, '2018-04-18 15:32:19.100759', 'Monitor', 74.0)\n",
      "(17, '2018-04-18 15:32:21.120451', 'Monitor', 86.0)\n",
      "(18, '2018-04-18 15:32:22.130355', 'Monitor', 57.0)\n",
      "(19, '2018-04-18 15:32:23.140021', 'Monitor', 53.0)\n",
      "(20, '2018-04-18 15:32:24.150483', 'Monitor', 78.0)\n"
     ]
    }
   ],
   "source": [
    "leitura_todos_dados()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obrigado - Data Science Academy - <a href=\"http://facebook.com/dsacademybr\">facebook.com/dsacademybr</a>"
   ]
  }
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