{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Data Science Library Integrations"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Overview\n",
    "`redshift_connector` provides integration with [pandas](https://github.com/pandas-dev/pandas) and [numpy](https://github.com/numpy/numpy) to enable everyone who works with data a simple interface for reading and writing data to and from Amazon Redshift."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Retrieving data as a `pandas.DataFrame`\n",
    "`redshift_connector`'s `fetch_dataframe()` method allows users to directly retrieve result sets as a `pandas.DataFrame`. An example workflow using this function is shown below"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import pandas\n",
    "import redshift_connector\n",
    "with redshift_connector.connect(\n",
    "    host='examplecluster.abc123xyz789.us-west-1.redshift.amazonaws.com',\n",
    "    database='dev',\n",
    "    user='awsuser',\n",
    "    password='my_password'\n",
    ") as conn:\n",
    "    with conn.cursor() as cursor:\n",
    "        cursor.execute(\"create Temp table book(bookname varchar,author‎ varchar)\")\n",
    "        cursor.executemany(\"insert into book (bookname, author‎) values (%s, %s)\",\n",
    "                           [\n",
    "                               ('One Hundred Years of Solitude', 'Gabriel García Márquez'),\n",
    "                               ('A Brief History of Time', 'Stephen Hawking')\n",
    "\n",
    "                           ])\n",
    "        cursor.execute(\"select * from book\")\n",
    "        result: pandas.DataFrame = cursor.fetch_dataframe()\n",
    "        print(result)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Retrieving data as a `numpy.ndarray`\n",
    "`redshift_connector`'s `fetch_numpy_array()` method allows users to directly retrieve result sets as a `numpy.ndarray`. An example workflow using this function is shown below"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import numpy\n",
    "import redshift_connector\n",
    "with redshift_connector.connect(\n",
    "    host='examplecluster.abc123xyz789.us-west-1.redshift.amazonaws.com',\n",
    "    database='dev',\n",
    "    user='awsuser',\n",
    "    password='my_password'\n",
    ") as conn:\n",
    "    with conn.cursor() as cursor:\n",
    "        cursor.execute(\"create Temp table book(bookname varchar,author‎ varchar)\")\n",
    "        cursor.executemany(\"insert into book (bookname, author‎) values (%s, %s)\",\n",
    "                           [\n",
    "                               ('One Hundred Years of Solitude', 'Gabriel García Márquez'),\n",
    "                               ('A Brief History of Time', 'Stephen Hawking')\n",
    "\n",
    "                           ])\n",
    "        cursor.execute(\"select * from book\")\n",
    "        result: numpy.ndarray = cursor.fetch_numpy_array()\n",
    "        print(result)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Inserting a `pandas.DataFrame` into an Amazon Redshift database table\n",
    "`redshift_connector`'s `write_dataframe()` method allows users to directly insert a`pandas.DataFrame` to a database table. An example workflow using this function is shown below"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import redshift_connector\n",
    "\n",
    "df: pd.DataFrame = pd.DataFrame(\n",
    "    np.array(\n",
    "        [\n",
    "            [\"One Hundred Years of Solitude\", \"Gabriel García Márquez\"],\n",
    "            [\"A Brief History of Time\", \"Stephen Hawking\"],\n",
    "        ]\n",
    "    ),\n",
    "    columns=[\"bookname\", \"author‎\"],\n",
    ")\n",
    "\n",
    "with redshift_connector.connect(\n",
    "    host='examplecluster.abc123xyz789.us-west-1.redshift.amazonaws.com',\n",
    "    database='dev',\n",
    "    user='awsuser',\n",
    "    password='my_password'\n",
    ") as conn:\n",
    "    with conn.cursor() as cursor:\n",
    "        cursor.execute(\"create Temp table book(bookname varchar,author‎ varchar)\")\n",
    "        cursor.write_dataframe(df, \"book\")\n",
    "        cursor.execute(\"select * from book;\")\n",
    "        result = cursor.fetchall()\n",
    "        print(result)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}