{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas + SQLAlchemy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 這邊先講結論\n",
    "* 連線\n",
    "  * 一率用 sqlalchemy 的 `create_engine` 來定義連線 engine，各種資料庫都是 (mysql, mssql, ...)。例如： `engine = create_engine(\"mysql+pymysql://root:my-secret-pw@localhost:3307/mydev\")`\n",
    "  * 用 conn 來建立連線，例如 `conn = engine.connect()`\n",
    "* 可以直接用 raw sql 的語句，但記得在前面加上 sqlalchemy.sql 裡的 text()，才不會讓 sql 的特殊符號被影響到。例如 `sql_string = \"select * from table_A where col_1 like 'C%';\"`，那 `%` 在 sql alchemy 中會被當作特殊字元，且沒有逃脫符號可以幫你忙，所以正確做法是這樣： `sql_string2 = text(sql_string)`，再拿 sql_string2 去做事。\n",
    "* raw sql 的寫法，就直接用在 CRUD 中，例如：\n",
    "  * \"改\", \"刪\"，例如 `conn.execute(\"update table_A set col_1 = 17;\")`\n",
    "  * \"讀\"，不會用 `conn.execute(sql_string2)` 的作法，因為他回傳的不是我想要的 pandas dataframe。會用 `pd.read_sql(sql_string2, engine)` 這種方式來做。得到的就會是 pd.DataFrame。但不變的是，我還是用 raw sql 語句，只是丟給不同 function 來做而已。\n",
    "  * CRUD 裡面的 \"增\"，你可以用懶人法 `pd.DataFrame.to_sql()`直接傳上去，缺點就是沒有 schema，所以他自動幫你生的 schema 就是文字和數字而已，沒有 datetime。如果要更細緻的做，就直接用 raw sql 語句，做法就是和剛剛一樣： `conn.execute(sql_string)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "from sqlalchemy.sql import text\n",
    "import pymysql\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 連線"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"mysql+pymysql://root:my-secret-pw@localhost:3307/mydev\")\n",
    "conn = engine.connect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## [增]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用 `pd.DataFrame.to_sql()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 這是最簡單的方式，也是之前在 R 的時候使用的方式\n",
    "* 簡單來說，就是不管 sql table schema，全都用 文字, 數字 的形式來存。犧牲 datetime 的格式。\n",
    "* 有關時間的操作，都是從 sql 拉到 local 變成 dataframe 後，再自己轉成時間格式來處理\n",
    "* 之後可以再查查看，應該有機會也可以定義 時間, ENUM, SET 等格式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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>username</th>\n",
       "      <th>height</th>\n",
       "      <th>blood_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hank</td>\n",
       "      <td>185</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mike</td>\n",
       "      <td>172</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pink</td>\n",
       "      <td>153</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>shark</td>\n",
       "      <td>198</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  username  height blood_type\n",
       "0     hank     185          O\n",
       "1     mike     172          A\n",
       "2     pink     153          B\n",
       "3    shark     198          O"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user2 = pd.DataFrame({\n",
    "    \"username\": [\"hank\",\"mike\",\"pink\",\"shark\"],\n",
    "    \"height\": [185, 172, 153, 198],\n",
    "    \"blood_type\":[\"O\", \"A\", \"B\", \"O\"]\n",
    "})\n",
    "user2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user2.to_sql(\n",
    "    name = \"user2\", # table name\n",
    "    con = engine,\n",
    "    if_exists = \"replace\", # 'fail', 'replace' 就是 overwrite, 'append'\n",
    "    index = False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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>username</th>\n",
       "      <th>height</th>\n",
       "      <th>blood_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hank</td>\n",
       "      <td>185</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mike</td>\n",
       "      <td>172</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pink</td>\n",
       "      <td>153</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>shark</td>\n",
       "      <td>198</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  username  height blood_type\n",
       "0     hank     185          O\n",
       "1     mike     172          A\n",
       "2     pink     153          B\n",
       "3    shark     198          O"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"select * from user2\", engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = pd.DataFrame({\n",
    "    \"username\": [\"pinpin\",\"mimi\"],\n",
    "    \"height\": [160, 170],\n",
    "    \"blood_type\": [\"B\", \"B\"]\n",
    "})\n",
    "new.to_sql(\"user2\", engine, if_exists=\"append\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>username</th>\n",
       "      <th>height</th>\n",
       "      <th>blood_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hank</td>\n",
       "      <td>185</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mike</td>\n",
       "      <td>172</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pink</td>\n",
       "      <td>153</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>shark</td>\n",
       "      <td>198</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pinpin</td>\n",
       "      <td>160</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mimi</td>\n",
       "      <td>170</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  username  height blood_type\n",
       "0     hank     185          O\n",
       "1     mike     172          A\n",
       "2     pink     153          B\n",
       "3    shark     198          O\n",
       "4   pinpin     160          B\n",
       "5     mimi     170          B"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"select * from user2\", engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用 sql 語句來做"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 這種作法對熟悉 sql 的人來說蠻直覺的，因為就完全使用 sql 的語句來做"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x11adf54c0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_string = text('''\n",
    "CREATE TABLE IF NOT EXISTS employee(\n",
    "  id INT NOT NULL AUTO_INCREMENT,\n",
    "  first_name VARCHAR(100) NOT NULL,\n",
    "  last_name VARCHAR(100) NOT NULL,\n",
    "  title VARCHAR(100) DEFAULT NULL,\n",
    "  salary DOUBLE DEFAULT NULL,\n",
    "  hire_date DATE NOT NULL,\n",
    "  notes TEXT,\n",
    "  PRIMARY KEY (id)\n",
    ");\n",
    "''')\n",
    "conn.execute(sql_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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>id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>title</th>\n",
       "      <th>salary</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, first_name, last_name, title, salary, hire_date, notes]\n",
       "Index: []"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"select * from employee\", engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x107a25fa0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_string = text('''\n",
    "INSERT INTO employee (first_name, last_name, title, salary, hire_date) VALUES \n",
    "    ('Robin', 'Jackman', 'Software Engineer', 5500, '2001-10-12'),\n",
    "    ('Taylor', 'Edward', 'Software Architect', 7200, '2002-09-21'),\n",
    "    ('Vivian', 'Dickens', 'Database Administrator', 6000, '2012-08-29'),\n",
    "    ('Harry', 'Clifford', 'Database Administrator', 6800, '2015-12-10'),\n",
    "    ('Eliza', 'Clifford', 'Software Engineer', 4750, '1998-10-19'),\n",
    "    ('Nancy', 'Newman', 'Software Engineer', 5100, '2007-01-23'),\n",
    "    ('Melinda', 'Clifford', 'Project Manager', 8500, '2013-10-29'),\n",
    "    ('Jack', 'Chan', 'Test Engineer', 6500, '2018-09-07'),\n",
    "    ('Harley', 'Gilbert', 'Software Architect', 8000, '2000-07-17');\n",
    "''')\n",
    "conn.execute(sql_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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>id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>title</th>\n",
       "      <th>salary</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Robin</td>\n",
       "      <td>Jackman</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>2001-10-12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>Edward</td>\n",
       "      <td>Software Architect</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>2002-09-21</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Vivian</td>\n",
       "      <td>Dickens</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2012-08-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Harry</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Eliza</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>1998-10-19</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Newman</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>2007-01-23</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Melinda</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Chan</td>\n",
       "      <td>Test Engineer</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Harley</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Software Architect</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>2000-07-17</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id first_name last_name                   title  salary   hire_date notes\n",
       "0   1      Robin   Jackman       Software Engineer  5500.0  2001-10-12  None\n",
       "1   2     Taylor    Edward      Software Architect  7200.0  2002-09-21  None\n",
       "2   3     Vivian   Dickens  Database Administrator  6000.0  2012-08-29  None\n",
       "3   4      Harry  Clifford  Database Administrator  6800.0  2015-12-10  None\n",
       "4   5      Eliza  Clifford       Software Engineer  4750.0  1998-10-19  None\n",
       "5   6      Nancy    Newman       Software Engineer  5100.0  2007-01-23  None\n",
       "6   7    Melinda  Clifford         Project Manager  8500.0  2013-10-29  None\n",
       "7   8       Jack      Chan           Test Engineer  6500.0  2018-09-07  None\n",
       "8   9     Harley   Gilbert      Software Architect  8000.0  2000-07-17  None"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"select * from employee\", engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 這邊補充一些 sql 語句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 寫明 欄位名稱 和 值，如果該表有其他欄位沒被提到，就用 NULL 或 default 帶入\n",
    "sql_string = text('''\n",
    "INSERT INTO person(name, phone, age) VALUES (\"ABC\", \"1234\", 20);\n",
    "''')\n",
    "\n",
    "sql_string = text('''\n",
    "INSERT INTO person(name, phone, age) VALUES \n",
    "    (\"ABC\", \"1234\", 20),\n",
    "    (\"DEF\", \"5678\", 21);\n",
    "''')\n",
    "\n",
    "# 沒寫明欄位名稱，那就是默認要照該表所有欄位順序來做\n",
    "sql_string = text('''\n",
    "INSERT INTO person VALUES (\"ABC\", \"1234\", 20);\n",
    "''')\n",
    "\n",
    "sql_string = text('''\n",
    "INSERT INTO person VALUES \n",
    "    (\"ABC\", \"1234\", 20),\n",
    "    (\"DEF\", \"5678\", 21);\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## [查] Read"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用 `pd.read_sql()` 查整張表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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",
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       "    }\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>id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>title</th>\n",
       "      <th>salary</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Robin</td>\n",
       "      <td>Jackman</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>2001-10-12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>Edward</td>\n",
       "      <td>Software Architect</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>2002-09-21</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Vivian</td>\n",
       "      <td>Dickens</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2012-08-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Harry</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Eliza</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>1998-10-19</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Newman</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>2007-01-23</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Melinda</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Chan</td>\n",
       "      <td>Test Engineer</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Harley</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Software Architect</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>2000-07-17</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id first_name last_name                   title  salary  hire_date notes\n",
       "0   1      Robin   Jackman       Software Engineer  5500.0 2001-10-12  None\n",
       "1   2     Taylor    Edward      Software Architect  7200.0 2002-09-21  None\n",
       "2   3     Vivian   Dickens  Database Administrator  6000.0 2012-08-29  None\n",
       "3   4      Harry  Clifford  Database Administrator  6800.0 2015-12-10  None\n",
       "4   5      Eliza  Clifford       Software Engineer  4750.0 1998-10-19  None\n",
       "5   6      Nancy    Newman       Software Engineer  5100.0 2007-01-23  None\n",
       "6   7    Melinda  Clifford         Project Manager  8500.0 2013-10-29  None\n",
       "7   8       Jack      Chan           Test Engineer  6500.0 2018-09-07  None\n",
       "8   9     Harley   Gilbert      Software Architect  8000.0 2000-07-17  None"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"employee\", engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用 sql 語句 (推薦)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 一樣，繼續用 `pd.read_sql()` 就好，你丟 sql 字串進去就可以了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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>id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>title</th>\n",
       "      <th>salary</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Harry</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>Melinda</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id first_name last_name                   title  salary   hire_date notes\n",
       "0   4      Harry  Clifford  Database Administrator  6800.0  2015-12-10  None\n",
       "1   7    Melinda  Clifford         Project Manager  8500.0  2013-10-29  None"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_string = text('''\n",
    "SELECT *\n",
    "FROM employee\n",
    "WHERE salary > 5000 and last_name = \"Clifford\"\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 既然可以下 sql 指令，那各種 order by, filter, group_by, aggregate 的整令，當然就都可以下了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### order by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>last_name</th>\n",
       "      <th>title</th>\n",
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       "      <th>notes</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>Eliza</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>1998-10-19</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Newman</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>2007-01-23</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Robin</td>\n",
       "      <td>Jackman</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>2001-10-12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Vivian</td>\n",
       "      <td>Dickens</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2012-08-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Chan</td>\n",
       "      <td>Test Engineer</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>Harry</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>Edward</td>\n",
       "      <td>Software Architect</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>2002-09-21</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>Harley</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Software Architect</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>2000-07-17</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>Melinda</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id first_name last_name                   title  salary   hire_date notes\n",
       "0   5      Eliza  Clifford       Software Engineer  4750.0  1998-10-19  None\n",
       "1   6      Nancy    Newman       Software Engineer  5100.0  2007-01-23  None\n",
       "2   1      Robin   Jackman       Software Engineer  5500.0  2001-10-12  None\n",
       "3   3     Vivian   Dickens  Database Administrator  6000.0  2012-08-29  None\n",
       "4   8       Jack      Chan           Test Engineer  6500.0  2018-09-07  None\n",
       "5   4      Harry  Clifford  Database Administrator  6800.0  2015-12-10  None\n",
       "6   2     Taylor    Edward      Software Architect  7200.0  2002-09-21  None\n",
       "7   9     Harley   Gilbert      Software Architect  8000.0  2000-07-17  None\n",
       "8   7    Melinda  Clifford         Project Manager  8500.0  2013-10-29  None"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_string = text('''\n",
    "select * from employee\n",
    "order by salary;\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>title</th>\n",
       "      <th>salary</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>Melinda</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>Harley</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Software Architect</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>2000-07-17</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>Edward</td>\n",
       "      <td>Software Architect</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>2002-09-21</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Harry</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Chan</td>\n",
       "      <td>Test Engineer</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>Vivian</td>\n",
       "      <td>Dickens</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2012-08-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Robin</td>\n",
       "      <td>Jackman</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>2001-10-12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Newman</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>2007-01-23</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>Eliza</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>1998-10-19</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id first_name last_name                   title  salary   hire_date notes\n",
       "0   7    Melinda  Clifford         Project Manager  8500.0  2013-10-29  None\n",
       "1   9     Harley   Gilbert      Software Architect  8000.0  2000-07-17  None\n",
       "2   2     Taylor    Edward      Software Architect  7200.0  2002-09-21  None\n",
       "3   4      Harry  Clifford  Database Administrator  6800.0  2015-12-10  None\n",
       "4   8       Jack      Chan           Test Engineer  6500.0  2018-09-07  None\n",
       "5   3     Vivian   Dickens  Database Administrator  6000.0  2012-08-29  None\n",
       "6   1      Robin   Jackman       Software Engineer  5500.0  2001-10-12  None\n",
       "7   6      Nancy    Newman       Software Engineer  5100.0  2007-01-23  None\n",
       "8   5      Eliza  Clifford       Software Engineer  4750.0  1998-10-19  None"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_string = text('''\n",
    "select * from employee\n",
    "order by salary desc;\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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>id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>title</th>\n",
       "      <th>salary</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>Melinda</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>Harley</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Software Architect</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>2000-07-17</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>Edward</td>\n",
       "      <td>Software Architect</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>2002-09-21</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Harry</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Chan</td>\n",
       "      <td>Test Engineer</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>Vivian</td>\n",
       "      <td>Dickens</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2012-08-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Robin</td>\n",
       "      <td>Jackman</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>2001-10-12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Newman</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>2007-01-23</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>Eliza</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>1998-10-19</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id first_name last_name                   title  salary   hire_date notes\n",
       "0   7    Melinda  Clifford         Project Manager  8500.0  2013-10-29  None\n",
       "1   9     Harley   Gilbert      Software Architect  8000.0  2000-07-17  None\n",
       "2   2     Taylor    Edward      Software Architect  7200.0  2002-09-21  None\n",
       "3   4      Harry  Clifford  Database Administrator  6800.0  2015-12-10  None\n",
       "4   8       Jack      Chan           Test Engineer  6500.0  2018-09-07  None\n",
       "5   3     Vivian   Dickens  Database Administrator  6000.0  2012-08-29  None\n",
       "6   1      Robin   Jackman       Software Engineer  5500.0  2001-10-12  None\n",
       "7   6      Nancy    Newman       Software Engineer  5100.0  2007-01-23  None\n",
       "8   5      Eliza  Clifford       Software Engineer  4750.0  1998-10-19  None"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_string = text('''\n",
    "select * from employee\n",
    "order by salary desc, first_name;\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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>id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>title</th>\n",
       "      <th>salary</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Robin</td>\n",
       "      <td>Jackman</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>2001-10-12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>Edward</td>\n",
       "      <td>Software Architect</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>2002-09-21</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Vivian</td>\n",
       "      <td>Dickens</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>2012-08-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id first_name last_name                   title  salary   hire_date notes\n",
       "0   1      Robin   Jackman       Software Engineer  5500.0  2001-10-12  None\n",
       "1   2     Taylor    Edward      Software Architect  7200.0  2002-09-21  None\n",
       "2   3     Vivian   Dickens  Database Administrator  6000.0  2012-08-29  None"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(text(\"select * from employee limit 3;\"), engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 就是 R 的 `str_detect()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 常用語法\n",
    "sql_string1 = text(\"select * from employee where last_name like 'C%';\") # C開頭的，%表隨意字元不限字數\n",
    "sql_string2 = text(\"select * from employee where last_name like '%i%';\") # 中間出現過 i 的\n",
    "sql_string3 = text(\"select * from employee where last_name like '%an';\") # an 結尾的\n",
    "sql_string4 = text(\"select * from employee where last_name like '__an';\") # 知道長度是4，結尾是 an\n",
    "sql_string5 = text(\"select * from employee where last_name like 'J______';\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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>id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>title</th>\n",
       "      <th>salary</th>\n",
       "      <th>hire_date</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Harry</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Eliza</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>1998-10-19</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>Melinda</td>\n",
       "      <td>Clifford</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>2013-10-29</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>Jack</td>\n",
       "      <td>Chan</td>\n",
       "      <td>Test Engineer</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id first_name last_name                   title  salary   hire_date notes\n",
       "0   4      Harry  Clifford  Database Administrator  6800.0  2015-12-10  None\n",
       "1   5      Eliza  Clifford       Software Engineer  4750.0  1998-10-19  None\n",
       "2   7    Melinda  Clifford         Project Manager  8500.0  2013-10-29  None\n",
       "3   8       Jack      Chan           Test Engineer  6500.0  2018-09-07  None"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(sql_string1, engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### count(*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "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>count(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count(*)\n",
       "0         9"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 整張 table 有幾列\n",
    "sql_string = text('''\n",
    "select count(*) from employee;\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "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>count(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count(*)\n",
       "0         3"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# title 為 Software Engineer 的table，列數有幾列\n",
    "sql_string = text('''\n",
    "select count(*) from employee where title=\"Software Engineer\";\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "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>title</th>\n",
       "      <th>count(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Project Manager</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Software Architect</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Test Engineer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    title  count(*)\n",
       "0  Database Administrator         2\n",
       "1         Project Manager         1\n",
       "2      Software Architect         2\n",
       "3       Software Engineer         3\n",
       "4           Test Engineer         1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 各種 title 各有幾列\n",
    "sql_string = text('''\n",
    "select title, count(*) from employee\n",
    "group by title;\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### distinct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "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>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Software Engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Software Architect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Database Administrator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Project Manager</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Test Engineer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    title\n",
       "0       Software Engineer\n",
       "1      Software Architect\n",
       "2  Database Administrator\n",
       "3         Project Manager\n",
       "4           Test Engineer"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 有哪些 title?\n",
    "sql_string = text('''\n",
    "select distinct(title) from employee;\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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>count(distinct(title))</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count(distinct(title))\n",
       "0                       5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 有多少種 title\n",
    "sql_string = text('''\n",
    "select count(distinct(title)) from employee;\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### min, max, sum, avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "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>min(salary)</th>\n",
       "      <th>max(salary)</th>\n",
       "      <th>sum(salary)</th>\n",
       "      <th>avg(salary)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4750.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>58350.0</td>\n",
       "      <td>6483.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   min(salary)  max(salary)  sum(salary)  avg(salary)\n",
       "0       4750.0       8500.0      58350.0  6483.333333"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平均薪水, 最低薪水, 最高薪水, 薪水總合 各是多少\n",
    "sql_string = text('''\n",
    "select min(salary), max(salary), sum(salary), avg(salary) from employee;\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "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>min_salary</th>\n",
       "      <th>max_salary</th>\n",
       "      <th>sum_salary</th>\n",
       "      <th>avg_salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4750.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>58350.0</td>\n",
       "      <td>6483.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   min_salary  max_salary  sum_salary   avg_salary\n",
       "0      4750.0      8500.0     58350.0  6483.333333"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 換個名稱比較好讀\n",
    "sql_string = text('''\n",
    "select min(salary) as min_salary, \n",
    "    max(salary) as max_salary, \n",
    "    sum(salary) as sum_salary, \n",
    "    avg(salary) as avg_salary\n",
    "from employee;\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "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>title</th>\n",
       "      <th>n</th>\n",
       "      <th>min_salary</th>\n",
       "      <th>max_salary</th>\n",
       "      <th>sum_salary</th>\n",
       "      <th>avg_salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Project Manager</td>\n",
       "      <td>1</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>8500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Software Architect</td>\n",
       "      <td>2</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>7600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Test Engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>6500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>2</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>12800.0</td>\n",
       "      <td>6400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>3</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>15350.0</td>\n",
       "      <td>5116.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    title  n  min_salary  max_salary  sum_salary   avg_salary\n",
       "0         Project Manager  1      8500.0      8500.0      8500.0  8500.000000\n",
       "1      Software Architect  2      7200.0      8000.0     15200.0  7600.000000\n",
       "2           Test Engineer  1      6500.0      6500.0      6500.0  6500.000000\n",
       "3  Database Administrator  2      6000.0      6800.0     12800.0  6400.000000\n",
       "4       Software Engineer  3      4750.0      5500.0     15350.0  5116.666667"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 各種 title 的薪水比較\n",
    "sql_string = text('''\n",
    "select title,\n",
    "    count(*) as n,\n",
    "    min(salary) as min_salary, \n",
    "    max(salary) as max_salary, \n",
    "    sum(salary) as sum_salary, \n",
    "    avg(salary) as avg_salary\n",
    "from employee\n",
    "group by title\n",
    "order by avg_salary desc;\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### having"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 這邊只有一個觀念：想在 group_by+summarise 後，想對結果的資料表做篩選，那要用 having，不能用 where\n",
    "* 所以，having就是 group by 後，拿來做篩選用的 where。\n",
    "* 結論：\n",
    "  * 要這樣寫：  `select title, count(*) as n from employee group by title having n>1;`\n",
    "  * 不能這樣寫： `select title, count(*) as n from employee group by title where n>1;`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "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>title</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Database Administrator</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Software Architect</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    title  n\n",
       "0  Database Administrator  2\n",
       "1      Software Architect  2\n",
       "2       Software Engineer  3"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_string = text('''\n",
    "select title, count(*) as n from employee group by title having n>1;\n",
    "''')\n",
    "pd.read_sql(sql_string, engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### logic operators"
   ]
  },
  {
   "attachments": {
    "e95dd848-67c6-4fbb-8e0a-7ecf281ebdcb.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:e95dd848-67c6-4fbb-8e0a-7ecf281ebdcb.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [改] Update"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 因為 pandas 只有做 讀 和 寫 的 wrapper，分別是 `pd.DataFrame.read_sql()` 和 `pd.DataFrame.to_sql()`\n",
    "* 對於 update 和 delete，之前在 R 裡面是用 `dbSendUpdate(sql)`，就是直接執行 sql 指令\n",
    "* 在 python，就要先建立連線 `conn = engine.connect()`，建立後，就可以 execute 原生的 sql 語句 `conn.execute(sql_string)` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x11ae9bd90>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_string = text('''\n",
    "update user2\n",
    "set username = 'hank2'\n",
    "where height = 185\n",
    "''')\n",
    "\n",
    "conn.execute(sql_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "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>username</th>\n",
       "      <th>height</th>\n",
       "      <th>blood_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hank2</td>\n",
       "      <td>185</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mike</td>\n",
       "      <td>172</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pink</td>\n",
       "      <td>153</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>shark</td>\n",
       "      <td>198</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pinpin</td>\n",
       "      <td>160</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mimi</td>\n",
       "      <td>170</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  username  height blood_type\n",
       "0    hank2     185          O\n",
       "1     mike     172          A\n",
       "2     pink     153          B\n",
       "3    shark     198          O\n",
       "4   pinpin     160          B\n",
       "5     mimi     170          B"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"select * from user2\", engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [刪] Delete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 刪除 table 中的所有列 (但表和 schema 還在)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x11ae70550>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_string = text('''\n",
    "delete from user2\n",
    "''')\n",
    "\n",
    "# 刪除所有列\n",
    "# delete from user2\n",
    "\n",
    "# 刪除某些列\n",
    "# delete from user2 where xxx\n",
    "\n",
    "conn.execute(sql_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "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>username</th>\n",
       "      <th>height</th>\n",
       "      <th>blood_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [username, height, blood_type]\n",
       "Index: []"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"select * from user2\", engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 刪除整張表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlalchemy.engine.cursor.LegacyCursorResult at 0x11af145e0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql_string = text('''\n",
    "drop table user2\n",
    "''')\n",
    "\n",
    "conn.execute(sql_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.read_sql(\"select * from user2\", engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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