PandasMySQLSSH.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query your database over an SSH tunnel with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Blog post related to this notebook is here:<br>\n",
    "https://medium.com/@amirziai/query-your-database-over-an-ssh-tunnel-with-pandas-603ce49b35a1#.4dzdcrl48"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SSH and database information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ssh variables\n",
    "host = '52.xx.xx.xx'\n",
    "localhost = '127.0.0.1'\n",
    "ssh_username = 'ubuntu'\n",
    "ss_private_key = '/path/to/key.pem'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# database variables\n",
    "user='user'\n",
    "password='verysecurepassword'\n",
    "database='database'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install sshtunnel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!pip install sshtunnel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sshtunnel import SSHTunnelForwarder\n",
    "import MySQLdb as db\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The query function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def query(q):\n",
    "    with SSHTunnelForwarder(\n",
    "        (host, 22),\n",
    "        ssh_username=ssh_username,\n",
    "        ssh_private_key=private_key,\n",
    "        remote_bind_address=(localhost, 3306)\n",
    "    ) as server:\n",
    "        conn = db.connect(host=localhost,\n",
    "                               port=server.local_bind_port,\n",
    "                               user=user,\n",
    "                               passwd=password,\n",
    "                               db=database)\n",
    "\n",
    "        return pd.read_sql_query(q, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Query time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = query('select * from titanic limit 5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex  Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \\\n",
       "0    male   22      1      0         A/5 21171   7.2500   NaN        S   \n",
       "1  female   38      1      0          PC 17599  71.2833   C85        C   \n",
       "2  female   26      0      0  STON/O2. 3101282   7.9250   NaN        S   \n",
       "3  female   35      1      0            113803  53.1000  C123        S   \n",
       "4    male   35      0      0            373450   8.0500   NaN        S   \n",
       "\n",
       "   Survived  \n",
       "0         0  \n",
       "1         1  \n",
       "2         1  \n",
       "3         1  \n",
       "4         0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  }
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