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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Writing to AWS S3\n",
+    "\n",
+    "We have setup our demo environment with the label **aws** passed to reference our s3 access_key and secret_key and file. In the cell below we will write the data to our aws s3 bucket named **com.phi.demo**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.2.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "#\n",
+    "# Writing to mongodb database\n",
+    "#\n",
+    "import transport\n",
+    "from transport import providers\n",
+    "import pandas as pd\n",
+    "_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
+    "mgw = transport.get.writer(label='aws',file='friends.csv',bucket='com.phi.demo')\n",
+    "mgw.write(_data)\n",
+    "print (transport.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Reading from AWS S3\n",
+    "\n",
+    "The cell below reads the data that has been written by the cell above and computes the average age within a mongodb pipeline. The code in the background executes an aggregation using\n",
+    "\n",
+    "- Basic read of the designated file **friends.csv**\n",
+    "- Compute average age using standard pandas functions\n",
+    "\n",
+    "**NOTE**\n",
+    "\n",
+    "By design **read** object are separated from **write** objects in order to avoid accidental writes to the database.\n",
+    "Read objects are created with **transport.get.reader** whereas write objects are created with **transport.get.writer**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          bname  age\n",
+      "0    James Bond   55\n",
+      "1  Steve Rogers  150\n",
+      "2  Steve Nyemba   44\n",
+      "--------- STATISTICS ------------\n",
+      "83.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import transport\n",
+    "from transport import providers\n",
+    "import pandas as pd\n",
+    "\n",
+    "def cast(stream) :\n",
+    "    print (stream)\n",
+    "    return pd.DataFrame(str(stream))\n",
+    "mgr = transport.get.reader(label='aws', bucket='com.phi.demo',file='friends.csv')\n",
+    "_df = mgr.read()\n",
+    "print (_df)\n",
+    "print ('--------- STATISTICS ------------')\n",
+    "print (_df.age.mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An **auth-file** is a file that contains database parameters used to access the database. \n",
+    "For code in shared environments, we recommend \n",
+    "\n",
+    "1. Having the **auth-file** stored on disk \n",
+    "2. and the location of the file is set to an environment variable.\n",
+    "\n",
+    "To generate a template of the **auth-file** open the **file generator wizard** found at visit https://healthcareio.the-phi.com/data-transport"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}