{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "#### Writing to Google Bigquery\n",
    "\n",
    "1. Insure you have a Google Bigquery service account key on disk\n",
    "2. The service key location is set as an environment variable **BQ_KEY**\n",
    "3. The dataset will be automatically created within the project associated with the service key\n",
    "\n",
    "The cell below creates a dataframe that will be stored within Google Bigquery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 10106.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['data transport version ', '2.0.4']\n"
     ]
    }
   ],
   "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",
    "\n",
    "PRIVATE_KEY = os.environ['BQ_KEY'] #-- location of the service key\n",
    "DATASET = 'demo'\n",
    "_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
    "bqw = transport.get.writer(provider=providers.BIGQUERY,dataset=DATASET,table='friends',private_key=PRIVATE_KEY)\n",
    "bqw.write(_data,if_exists='replace') #-- default is append\n",
    "print (['data transport version ', transport.__version__])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading from Google Bigquery\n",
    "\n",
    "The cell below reads the data that has been written by the cell above and computes the average age within a Google Bigquery (simple query). \n",
    "\n",
    "- Basic read of the designated table (friends) created above\n",
    "- Execute an aggregate SQL against the table\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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading: 100%|\u001b[32m██████████\u001b[0m|\n",
      "Downloading: 100%|\u001b[32m██████████\u001b[0m|\n",
      "           name  age\n",
      "0    James Bond   55\n",
      "1  Steve Rogers  150\n",
      "2  Steve Nyemba   44\n",
      "--------- STATISTICS ------------\n",
      "   _counts   f0_\n",
      "0        3  83.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import transport\n",
    "from transport import providers\n",
    "import os\n",
    "PRIVATE_KEY=os.environ['BQ_KEY']\n",
    "pgr = transport.get.reader(provider=providers.BIGQUERY,dataset='demo',table='friends',private_key=PRIVATE_KEY)\n",
    "_df = pgr.read()\n",
    "_query = 'SELECT COUNT(*) _counts, AVG(age) from demo.friends'\n",
    "_sdf = pgr.read(sql=_query)\n",
    "print (_df)\n",
    "print ('--------- STATISTICS ------------')\n",
    "print (_sdf)"
   ]
  },
  {
   "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": []
  }
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