{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00, 5440.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['data transport version ', '2.0.0']\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.factory.instance(provider=providers.BIGQUERY,dataset=DATASET,table='friends',context='write',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",
    "It is possible to use **transport.factory.instance** or **transport.instance** they are the same. It allows the maintainers to know that we used a factory design pattern."
   ]
  },
  {
   "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.instance(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": [
    "The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
    "\n",
    "**NOTE**:\n",
    "\n",
    "The auth_file is intended to be **JSON** formatted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dataset': 'demo', 'table': 'friends'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "{\n",
    "    \n",
    "    \"dataset\":\"demo\",\"table\":\"friends\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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