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   "source": [
    "#### Writing data-transport plugins\n",
    "\n",
    "The data-transport plugins are designed to automate pre/post processing i.e\n",
    "\n",
    "    - Read -> Post processing\n",
    "    - Write-> Pre processing\n",
    "    \n",
    "In this example we will assume, data and write both pre/post processing to any supported infrastructure. We will equally show how to specify the plugins within a configuration file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# Writing to Google Bigquery database\n",
    "#\n",
    "import transport\n",
    "from transport import providers\n",
    "import pandas as pd\n",
    "import os\n",
    "import shutil\n",
    "#\n",
    "#\n",
    "\n",
    "DATABASE = '/home/steve/tmp/demo.db3'\n",
    "if os.path.exists(DATABASE) :\n",
    "    os.remove(DATABASE)\n",
    "#\n",
    "#    \n",
    "_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
    "litew = transport.get.writer(provider=providers.SQLITE,database=DATABASE)\n",
    "litew.write(_data,table='friends')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading from SQLite\n",
    "\n",
    "The cell below reads the data that has been written by the cell above and computes the average age from a plugin function we will write. \n",
    "\n",
    "- Basic read of the designated table (friends) created above\n",
    "- Read with pipeline functions defined in code\n",
    "\n",
    "**NOTE**\n",
    "\n",
    "It is possible to use **transport.factory.instance** or **transport.instance** or **transport.get.<[reader|writer]>** they are the same. It allows the maintainers to know that we used a factory design pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           name  age\n",
      "0    James Bond   55\n",
      "1  Steve Rogers  150\n",
      "2  Steve Nyemba   44\n",
      "\n",
      "\n",
      "           name   age  autoinc\n",
      "0    James Bond   5.5        0\n",
      "1  Steve Rogers  15.0        1\n",
      "2  Steve Nyemba   4.4        2\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import transport\n",
    "from transport import providers\n",
    "import os\n",
    "import numpy as np\n",
    "def _autoincrement (_data,**kwargs) :\n",
    "    \"\"\"\n",
    "    This function will add an autoincrement field to the table\n",
    "    \"\"\"\n",
    "    _data['autoinc'] = np.arange(_data.shape[0])\n",
    "    \n",
    "    return _data\n",
    "def reduce(_data,**_args) :\n",
    "    \"\"\"\n",
    "    This function will reduce the age of the data frame\n",
    "    \"\"\"\n",
    "    _data.age /= 10\n",
    "    return _data\n",
    "reader = transport.get.reader(provider=providers.SQLITE,database=DATABASE,table='friends')\n",
    "#\n",
    "# basic read of the data created in the first cell\n",
    "_df = reader.read()\n",
    "print (_df)\n",
    "print ()\n",
    "print()\n",
    "#\n",
    "# read of the data with pipeline function provided to alter the database\n",
    "print (reader.read(pipeline=[_autoincrement,reduce]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameters for instianciating a transport object (reader or writer) can be found at [data-transport home](https://healthcareio.the-phi.com/data-transport)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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