DSA-Python-Cap06-05-Update e Delete no SQLite.ipynb

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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": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Versão da Linguagem Python Usada Neste Jupyter Notebook: 3.7.6\n"
     ]
    }
   ],
   "source": [
    "# Versão da Linguagem Python\n",
    "from platform import python_version\n",
    "print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update e Delete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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, '02-05-2020', '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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "atualiza_dados()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, '2020-05-02 14:32:11', 'Teclado', 90.0)\n",
      "(11, '2020-03-23 19:17:26.170886', 'Monitor', 67.0)\n",
      "(12, '2020-03-23 19:17:27.177013', 'Monitor', 79.0)\n",
      "(13, '2020-03-23 19:17:28.179580', 'Monitor', 61.0)\n",
      "(14, '2020-03-23 19:17:29.181694', 'Monitor', 83.0)\n",
      "(15, '2020-03-23 19:17:30.188687', 'Monitor', 64.0)\n",
      "(16, '2020-03-23 19:17:31.192286', 'Monitor', 51.0)\n",
      "(17, '2020-03-23 19:17:32.199836', 'Monitor', 62.0)\n",
      "(18, '2020-03-23 19:17:33.202428', 'Monitor', 54.0)\n",
      "(19, '2020-03-23 19:17:34.209402', 'Monitor', 79.0)\n",
      "(20, '2020-03-23 19:17:35.215644', 'Monitor', 51.0)\n"
     ]
    }
   ],
   "source": [
    "leitura_todos_dados()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_dados()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, '2020-05-02 14:32:11', 'Teclado', 90.0)\n",
      "(11, '2020-03-23 19:17:26.170886', 'Monitor', 67.0)\n",
      "(12, '2020-03-23 19:17:27.177013', 'Monitor', 79.0)\n",
      "(13, '2020-03-23 19:17:28.179580', 'Monitor', 61.0)\n",
      "(14, '2020-03-23 19:17:29.181694', 'Monitor', 83.0)\n",
      "(15, '2020-03-23 19:17:30.188687', 'Monitor', 64.0)\n",
      "(16, '2020-03-23 19:17:31.192286', 'Monitor', 51.0)\n",
      "(18, '2020-03-23 19:17:33.202428', 'Monitor', 54.0)\n",
      "(19, '2020-03-23 19:17:34.209402', 'Monitor', 79.0)\n",
      "(20, '2020-03-23 19:17:35.215644', 'Monitor', 51.0)\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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